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Forecasting
Number of Followers: 1  Open Access journalISSN (Online) 2571-9394 Published by MDPI  [258 journals]
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- Forecasting, Vol. 5, Pages 127-137: Forecasting the Monkeypox Outbreak
Authors: Bowen Long, Fangya Tan, Mark Newman
First page: 127
Abstract: Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy.
Citation: Forecasting
PubDate: 2023-01-06
DOI: 10.3390/forecast5010005
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 138-152: Comparison of ARIMA, SutteARIMA, and
Holt-Winters, and NNAR Models to Predict Food Grain in India
Authors: Ansari Saleh Ahmar, Pawan Kumar Singh, R. Ruliana, Alok Kumar Pandey, Stuti Gupta
First page: 138
Abstract: The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025.
Citation: Forecasting
PubDate: 2023-01-10
DOI: 10.3390/forecast5010006
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 153-169: Coffee as an Identifier of Inflation
in Selected US Agglomerations
Authors: Marek Vochozka, Svatopluk Janek, Zuzana Rowland
First page: 153
Abstract: The research goal presented in this paper was to determine the strength of the relationship between the price of coffee traded on ICE Futures US and Consumer Price Indices in the major urban agglomerations of the United States—New York, Chicago, and Los Angeles—and to predict the future development. The results obtained using the Pearson correlation coefficient confirmed a very close direct correlation (r = 0.61 for New York and Chicago; r = 0.57 for Los Angeles) between the price of coffee and inflation. The prediction made using the SARIMA model disrupted the mutual correlation. The price of coffee is likely to anchor at a new level where it will fluctuate; on the other hand, the CPIs showed strong unilateral pro-growth trends. The results could be beneficial for the analysis and creation of policies and further analyses of market structures at the technical level.
Citation: Forecasting
PubDate: 2023-01-13
DOI: 10.3390/forecast5010007
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 170-171: Acknowledgment to the Reviewers of
Forecasting in 2022
Authors: Forecasting Editorial Office
First page: 170
Abstract: High-quality academic publishing is built on rigorous peer review [...]
Citation: Forecasting
PubDate: 2023-01-16
DOI: 10.3390/forecast5010008
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 172-195: Global Solar Radiation Forecasting
Based on Hybrid Model with Combinations of Meteorological Parameters:
Morocco Case Study
Authors: Brahim Belmahdi, Mohamed Louzazni, Mousa Marzband, Abdelmajid El Bouardi
First page: 172
Abstract: The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been developed over the past decades to improve the efficiency of predicting solar radiation with various input characteristics. This research provides five approaches for forecasting daily global solar radiation (GSR) in two Moroccan cities, Tetouan and Tangier. In this regard, autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), feed forward back propagation neural networks (FFBP), hybrid ARIMA-FFBP, and hybrid ARMA-FFBP were selected to compare and forecast the daily global solar radiation with different combinations of meteorological parameters. In addition, the performance in three approaches has been calculated in terms of the statistical metric correlation coefficient (R²), root means square error (RMSE), stand deviation (σ), the slope of best fit (SBF), legate’s coefficient of efficiency (LCE), and Wilmott’s index of agreement (WIA). The best model is selected by using the computed statistical metric, which is present, and the optimal value. The R² of the forecasted ARIMA, ARMA, FFBP, hybrid ARIMA-FFBP, and ARMA-FFBP models is varying between 0.9472% and 0.9931%. The range value of SPE is varying between 0.8435 and 0.9296. The range value of LCE is 0.8954 and 0.9696 and the range value of WIA is 0.9491 and 0.9945. The outcomes show that the hybrid ARIMA–FFBP and hybrid ARMA–FFBP techniques are more effective than other approaches due to the improved correlation coefficient (R2).
Citation: Forecasting
PubDate: 2023-01-27
DOI: 10.3390/forecast5010009
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 196-209: On Forecasting Cryptocurrency Prices:
A Comparison of Machine Learning, Deep Learning, and Ensembles
Authors: Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
First page: 196
Abstract: Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
Citation: Forecasting
PubDate: 2023-01-29
DOI: 10.3390/forecast5010010
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 210-212: Editorial for Special Issue:
“Tourism Forecasting: Time-Series Analysis of World and Regional
Data”
Authors: João Paulo Teixeira, Ulrich Gunter
First page: 210
Abstract: This Special Issue was honored with six contribution papers embracing the subject of tourism forecasting [...]
Citation: Forecasting
PubDate: 2023-02-02
DOI: 10.3390/forecast5010011
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 213-228: A Day-Ahead Photovoltaic Power
Prediction via Transfer Learning and Deep Neural Networks
Authors: Seyed Mahdi Miraftabzadeh, Cristian Giovanni Colombo, Michela Longo, Federica Foiadelli
First page: 213
Abstract: Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE.
Citation: Forecasting
PubDate: 2023-02-17
DOI: 10.3390/forecast5010012
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 229-255: Intervention Time Series Analysis and
Forecasting of Organ Donor Transplants in the US during the COVID-19 Era
Authors: Supraja Malladi, Qiqi Lu
First page: 229
Abstract: The COVID-19 pandemic has had a catastrophic effect on the healthcare system including organ transplants worldwide. The number of living donor transplants performed in the US was affected more significantly by the pandemic with a 22.6% decrease in counts from 2019 to 2020 due to concerns of unnecessarily exposing potential living donors and living donor recipients to possible COVID-19 infection. This paper examines donor transplant counts obtained from the United Network for Organ Sharing from January 2002 to August 2021 using an intervention time series model with March 2020 as the intervention event. Specifically, donor transplant counts are analyzed across the different organs, donor types, and some major individual sociocultural factors, which are potential conditions contributing to disparities in achieving donor transplant equity such as age, ethnicity, and gender. In addition, the kidney allocation policy implemented in March 2021 is introduced as a second intervention event for kidney donor transplants. Overall, forecasts generated by our methods are more accurate than those using seasonal autoregressive integrated moving average models without interventions and seasonal naive methods. The intervention time series model provides a forecast accuracy comparable to the exponential smoothing method.
Citation: Forecasting
PubDate: 2023-02-18
DOI: 10.3390/forecast5010013
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 256-284: Performance Analysis of Statistical,
Machine Learning and Deep Learning Models in Long-Term Forecasting of
Solar Power Production
Authors: Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington, Suhas Pol
First page: 256
Abstract: The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.
Citation: Forecasting
PubDate: 2023-02-22
DOI: 10.3390/forecast5010014
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 285-296: Assessing Spurious Correlations in Big
Search Data
Authors: Jesse T. Richman, Ryan J. Roberts
First page: 285
Abstract: Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors.
Citation: Forecasting
PubDate: 2023-02-28
DOI: 10.3390/forecast5010015
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 297-314: Day Ahead Electric Load Forecast: A
Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies
Authors: Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins, Sonia Leva
First page: 297
Abstract: Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks.
Citation: Forecasting
PubDate: 2023-03-02
DOI: 10.3390/forecast5010016
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 315-335: Time Series Dataset Survey for
Forecasting with Deep Learning
Authors: Yannik Hahn, Tristan Langer, Richard Meyes, Tobias Meisen
First page: 315
Abstract: Deep learning models have revolutionized research fields like computer vision and natural language processing by outperforming traditional models in multiple tasks. However, the field of time series analysis, especially time series forecasting, has not seen a similar revolution, despite forecasting being one of the most prominent tasks of predictive data analytics. One crucial problem for time series forecasting is the lack of large, domain-independent benchmark datasets and a competitive research environment, e.g., annual large-scale challenges, that would spur the development of new models, as was the case for CV and NLP. Furthermore, the focus of time series forecasting research is primarily domain-driven, resulting in many highly individual and domain-specific datasets. Consequently, the progress in the entire field is slowed down due to a lack of comparability across models trained on a single benchmark dataset and on a variety of different forecasting challenges. In this paper, we first explore this problem in more detail and derive the need for a comprehensive, domain-unspecific overview of the state-of-the-art of commonly used datasets for prediction tasks. In doing so, we provide an overview of these datasets and improve comparability in time series forecasting by introducing a method to find similar datasets which can be utilized to test a newly developed model. Ultimately, our survey paves the way towards developing a single widely used and accepted benchmark dataset for time series data, built on the various frequently used datasets surveyed in this paper.
Citation: Forecasting
PubDate: 2023-03-03
DOI: 10.3390/forecast5010017
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 336-350: Methodology for Optimizing Factors
Affecting Road Accidents in Poland
Authors: Piotr Gorzelanczyk, Henryk Tylicki
First page: 336
Abstract: With the rapid increase in the number of vehicles on the road, traffic accidents have become a rapidly growing threat, causing the loss of human life and economic assets. The reason for this is the rapid growth of the human population and the development of motorization. The main challenge in predicting and analyzing traffic accident data is the small size of the dataset that can be used for analysis in this regard. While traffic accidents cause, globally, millions of deaths and injuries each year, their density in time and space is low. The purpose of this article is to present a methodology for determining the role of factors influencing road accidents in Poland. For this purpose, multi-criteria optimization methods were used. The results obtained allow us to conclude that the proposed solution can be used to search for the best solution for the selection of factors affecting traffic accidents. Furthermore, based on the study, it can be concluded that the factors primarily influencing traffic accidents are weather conditions (fog, smoke, rainfall, snowfall, hail, or cloud cover), province (Lower Silesian, Lubelskie, Lodzkie, Malopolskie, Mazovian, Opolskie, Podkarpackie, Pomeranian, Silesian, Warmian-Masurian, and Greater Poland), and type of road (with two one-way carriageways; two-way, single carriageway road). Noteworthy is the fact that all days of the week also affect the number of vehicle accidents, although most of them occur on Fridays.
Citation: Forecasting
PubDate: 2023-03-07
DOI: 10.3390/forecast5010018
Issue No: Vol. 5, No. 1 (2023)
- Forecasting, Vol. 5, Pages 1-21: Extracting Statistical Properties of
Solar and Photovoltaic Power Production for the Scope of Building a
Sophisticated Forecasting Framework
Authors: Joseph Ndong, Ted Soubdhan
First page: 1
Abstract: Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation on which the energy production depends. A suitable forecasting framework might take into account this high variability and could be able to adjust/re-adjust model parameters to reduce sensitivity to estimation errors. The framework should also be able to re-adapt the model parameters whenever the atmospheric conditions change drastically or suddenly—this changes according to microscopic variations. This work presents a new methodology to analyze carefully the meaningful features of global solar radiation variability and extract some relevant information about the probabilistic laws which governs its dynamic evolution. The work establishes a framework able to identify the macroscopic variations from the solar irradiance. The different categories of variability correspond to different levels of meteorological conditions and events and can occur in different time intervals. Thereafter, the tool will be able to extract the abrupt changes, corresponding to microscopic variations, inside each level of variability. The methodology is based on a combination of probability and possibility theory. An unsupervised clustering technique based on a Gaussian mixture model is proposed to identify, first, the categories of variability and, using a hidden Markov model, we study the temporal dependency of the process to identify the dynamic evolution of the solar irradiance as different temporal states. Finally, by means of some transformations of probabilities to possibilities, we identify the abrupt changes in the solar radiation. The study is performed in Guadeloupe, where we have a long record of global solar radiation data recorded at 1 Hertz.
Citation: Forecasting
PubDate: 2022-12-29
DOI: 10.3390/forecast5010001
Issue No: Vol. 5, No. 1 (2022)
- Forecasting, Vol. 5, Pages 22-80: Comprehensive Review of Power Electronic
Converters in Electric Vehicle Applications
Authors: Rejaul Islam, S M Sajjad Hossain Rafin, Osama A. Mohammed
First page: 22
Abstract: Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, control schemes, output power, reliability, losses, switching frequency, operations, charging systems, advantages, and disadvantages. This article is intended to help engineers and researchers forecast typical recharging/discharging durations, the lifetime of energy storage with the help of control systems and machine learning, and the performance probability of using AlGaN/GaN heterojunction-based high-electron-mobility transistors (HEMTs) in EV systems. The analysis of this extensive review paper suggests that the Vienna rectifier provides significant performance among all AC–DC rectifier converters. Moreover, the multi-device interleaved DC–DC boost converter is best suited for the DC–DC conversion stage. Among DC–AC converters, the third harmonic injected seven-level inverter is found to be one of the best in EV driving. Furthermore, the utilization of multi-level inverters can terminate the requirement of the intermediate DC–DC converter. In addition, the current status, opportunities, challenges, and applications of wireless power transfer in hybrid and all-electric vehicles were also discussed in this paper. Moreover, the adoption of wide bandgap semiconductors was considered. Because of their higher power density, breakdown voltage, and switching frequency characteristics, a light yet efficient power converter design can be achieved for EVs. Finally, the article’s intent was to provide a reference for engineers and researchers in the automobile industry for forecasting calculations.
Citation: Forecasting
PubDate: 2022-12-29
DOI: 10.3390/forecast5010002
Issue No: Vol. 5, No. 1 (2022)
- Forecasting, Vol. 5, Pages 81-101: Machine Learning Models and Intra-Daily
Market Information for the Prediction of Italian Electricity Prices
Authors: Silvia Golia, Luigi Grossi, Matteo Pelagatti
First page: 81
Abstract: In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models.
Citation: Forecasting
PubDate: 2022-12-30
DOI: 10.3390/forecast5010003
Issue No: Vol. 5, No. 1 (2022)
- Forecasting, Vol. 5, Pages 102-126: Spatial Dependence of Average Prices
Authors: Venera Timiryanova, Irina Lakman, Vadim Prudnikov, Dina Krasnoselskaya
First page: 102
Abstract: The price of market products is the result of the interaction of supply and demand. However, within the same country, prices can vary significantly, especially during crisis periods. The purpose of this study is to identify patterns in the changing spatial dependence of the prices of certain product categories, namely pasta, potatoes, sugar, candies, poultry and butter. We used daily data from January 1, 2019, to March 31, 2022, and analyzed two important indicators: spatial variation and spatial autocorrelation of average daily prices. The analysis showed that spatial dependency changes over time and follows its own pattern for each product category. We recognized cyclic changes in spatial autocorrelation and noticed the effect of legislative restrictions on spatial correlations. It has been shown that the spatial variation of prices and spatial autocorrelation can change in different directions.
Citation: Forecasting
PubDate: 2022-12-31
DOI: 10.3390/forecast5010004
Issue No: Vol. 5, No. 1 (2022)
- Forecasting, Vol. 4, Pages 752-766: Forecasting Bitcoin Spikes: A
GARCH-SVM Approach
Authors: Theophilos Papadimitriou, Periklis Gogas, Athanasios Fotios Athanasiou
First page: 752
Abstract: This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as ‘spikes’. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones.
Citation: Forecasting
PubDate: 2022-09-22
DOI: 10.3390/forecast4040041
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 767-786: Big Data and Predictive Analytics for
Business Intelligence: A Bibliographic Study (2000–2021)
Authors: Yili Chen, Congdong Li, Han Wang
First page: 767
Abstract: Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.
Citation: Forecasting
PubDate: 2022-09-23
DOI: 10.3390/forecast4040042
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 787-797: Supervised and Unsupervised Machine
Learning Algorithms for Forecasting the Fracture Location in Dissimilar
Friction-Stir-Welded Joints
Authors: Akshansh Mishra, Anish Dasgupta
First page: 787
Abstract: Artificial-intelligence-based algorithms are used in manufacturing to automate difficult activities and discover workflow or process patterns that had never been noticed before. Recent studies deal with the forecasting of the fracture location in dissimilar friction-stir-welded AA5754–C11000 alloys. Four types of supervised machine-learning-based classification algorithms i.e., decision tree, logistic classification, random forest, and AdaBoost were implemented. Additionally, in the present work, for the first time, a neurobiological-based unsupervised machine learning algorithm, i.e., self-organizing map (SOM) neural network, is implemented for determining the fracture location in dissimilar friction-stir-welded AA5754–C11000 alloys. Tool shoulder diameter (mm), tool rotational speed (RPM), and tool traverse speed (mm/min) are input parameters, while the fracture location, i.e., whether the specimen’s fracture is in the thermo-mechanically affected zone (TMAZ) of copper, or if it fractures in the TMAZ of aluminium. The results show that out of all implemented algorithms, the SOM algorithm is able to predict the fracture location with the highest accuracy of 96.92%.
Citation: Forecasting
PubDate: 2022-09-29
DOI: 10.3390/forecast4040043
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 798-818: Evaluating the Comparative Accuracy of
COVID-19 Mortality Forecasts: An Analysis of the First-Wave Mortality
Forecasts in the United States
Authors: Rahul Pathak, Daniel Williams
First page: 798
Abstract: The sudden onset of the COVID-19 pandemic posed significant challenges for forecasting professionals worldwide. This article examines the early forecasts of COVID-19 transmission, using the context of the United States, one of the early epicenters of the crisis. The article compares the relative accuracy of selected models from two forecasters who informed government policy in the first three months of the pandemic, the Institute of Health Metrics and Evaluation (IHME) and Columbia University. Furthermore, we examine whether the forecasts improved as more data became available in the subsequent months of the pandemic, using the forecasts from Los Alamos National Laboratory and the University of Texas, Austin. The analysis focuses on mortality estimates and compares forecasts using epidemiological and curve-fitting models during the first wave of the pandemic from March 2020 to October 2020. As health agencies worldwide struggled with uncertainty in models and projections of COVID-19 caseload and mortality, this article provides important insights that can be useful for crafting policy responses to the ongoing pandemic and future outbreaks.
Citation: Forecasting
PubDate: 2022-09-29
DOI: 10.3390/forecast4040044
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 819-844: Sex Differential Dynamics in Coherent
Mortality Models
Authors: Snorre Jallbjørn, Søren Fiig Jarner
First page: 819
Abstract: The main purpose of coherent mortality models is to produce plausible, joint forecasts for related populations avoiding, e.g., crossing or diverging mortality trajectories; however, the coherence assumption is very restrictive and it enforces trends that may be at odds with data. In this paper we focus on coherent, two-sex mortality models and we prove, under suitable conditions, that the coherence assumption implies sex gap unimodality, i.e., we prove that the difference in life expectancy between women and men will first increase and then decrease. Moreover, we demonstrate that, in the model, the sex gap typically peaks when female life expectancy is between 30 to 50 years. This explains why coherent mortality models predict narrowing sex gaps for essentially all Western European countries and all jump-off years since the 1950s, despite the fact that the actual sex gap was widening until the 1980s. In light of these findings, we discuss the current role of coherence as the gold standard for multi-population mortality models.
Citation: Forecasting
PubDate: 2022-09-29
DOI: 10.3390/forecast4040045
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 845-865: Coupling a Neural Network with a
Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban
Rain Radars
Authors: Marino Marrocu, Luca Massidda
First page: 845
Abstract: Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable forecasts, which is especially useful for weather radars with limited spatial range, and that can be used operationally on devices with low computational capacity. The ensemble members are obtained by a novel stochastic noise generation process, consistent with the spatial scales not resolved by the prediction model, which allows continuous downscaling of the output of a deep generative neural network. Through an in-depth analysis of the results, for precipitation accumulated in the first hour, measured by all the most robust skill indicators, extended to an entire year of data at 5-min time resolution, we demonstrate that the proposed procedure is able to provide calibrated, reliable, and sharp ensemble rainfall forecasts with a quality comparable or superior to the state-of-the-art classical alternative optical flow technique. The ensemble generation procedure we propose is sufficiently general to be applied in principle to other deterministic architectures as well, thus enabling their generalization in probabilistic terms.
Citation: Forecasting
PubDate: 2022-10-28
DOI: 10.3390/forecast4040046
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 866-881: Has EU Accession Boosted Patent
Performance in the EU-13' A Critical Evaluation Using Causal Impact
Analysis with Bayesian Structural Time-Series Models
Authors: Agnieszka Kleszcz, Krzysztof Rusek
First page: 866
Abstract: This paper provides new insights into the causal effects of the enlargement of the European Union (EU) on patent performance. The study focuses on the new EU member states (EU-13) and accession is considered as an intervention whose causal effect is estimated by the causal impact method using a Bayesian structural time-series model (proposed by Google). The empirical results based on data collected from the OECD database from 1985–2017 point towards a conclusion that joining the EU has had a significant impact on patent performance in Romania, Estonia, Poland, the Czech Republic, Croatia and Lithuania, although in the latter two countries, the impact was negative. For the rest of the EU-13 countries, there is no significant effect on patent performance. Whether the EU accession effect is significant or not, the EU-13 are far behind the EU-15 (countries which entered the EU before 2004) in terms of patent performance. The majority of patents (98.66%) are assigned to the EU-15, with just 1.34% of assignees belonging to the EU-13.
Citation: Forecasting
PubDate: 2022-10-29
DOI: 10.3390/forecast4040047
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 882-903: Precision and Reliability of Forecasts
Performance Metrics
Authors: Philippe St-Aubin, Bruno Agard
First page: 882
Abstract: The selection of an accurate performance metric is highly important to evaluate the quality of a forecasting method. This evaluation may help to select between different forecasting tools of forecasting outputs, and then support many decisions within a company. This paper proposes to evaluate the sensitivity and reliability of forecasts performance metrics. The methodology is tested using multiple time series of different scales and demand patterns, such as intermittent demand. The idea is to add to each series a noise following a known distribution to represent forecasting models of a known error distribution. Varying the parameters of the distribution of the noise allows to evaluate how sensitive and reliable performance metrics are to changes in bias and variance of the error of a forecasting model. The experiments concluded that sRMSE is more reliable than MASE in most cases on those series. sRMSE is especially reliable for detecting changes in the variance of a model and sPIS is the most sensitive metric to the bias of a model. sAPIS is sensible to both variance and bias but is less reliable.
Citation: Forecasting
PubDate: 2022-10-30
DOI: 10.3390/forecast4040048
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 904-924: Forecasting Daily and Weekly Passenger
Demand for Urban Rail Transit Stations Based on a Time Series Model
Approach
Authors: Dung David Chuwang, Weiya Chen
First page: 904
Abstract: Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems and make decisions about train schedule patterns to improve operational efficiency, increase revenue management, and improve driving safety. The accuracy of the forecast results will directly affect the operation planning of urban rail transit (URT). Therefore, based on the collected inbound historical passenger data, this study used the Box–Jenkins time series with the Facebook Prophet algorithm to analyze the characteristics of urban rail transit passenger demand and achieved better computational forecasting performance accuracy. After analyzing the periodicity, correlation, and stationarity, different time series models were constructed. The Akaike information criteria (AIC), Bayesian information criteria (BIC), mean squared error (MSE), and root mean squared error (RMSE) were used to evaluate the adequacy of the best forecast model from among several tested candidates’ models for the Box–Jenkins. The parameters of the daily and weekly models were estimated using statistical software. The experimental results of this study are of both theoretical and practical significance to the urban rail transit (URT) station authorities for an effective station planning system. The forecasting results signify that the SARIMA (5, 1, 3) (1, 0, 0)24 model performs better and is more stable in forecasting the daily passenger demand, and the ARMA (2, 1) model performs better in forecasting the weekly passenger demand. When comparing the SARIMA and ARMA models with the Facebook Prophet, results show that the Facebook Prophet model is superior to the SARIMA model for the daily time series, and the ARMA model is superior to the Facebook Prophet model for the weekly time series.
Citation: Forecasting
PubDate: 2022-11-16
DOI: 10.3390/forecast4040049
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 925-935: Predicting Credit Scores with Boosted
Decision Trees
Authors: João A. Bastos
First page: 925
Abstract: Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The prediction accuracy of boosted decision trees is benchmarked against two alternative machine learning techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models.
Citation: Forecasting
PubDate: 2022-11-17
DOI: 10.3390/forecast4040050
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 936-948: Predictive Data Analytics for
Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid
Authors: Nasir Ayub, Usman Ali, Kainat Mustafa, Syed Muhammad Mohsin, Sheraz Aslam
First page: 936
Abstract: In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.
Citation: Forecasting
PubDate: 2022-11-21
DOI: 10.3390/forecast4040051
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 949-968: Systematic Assessment of the Effects
of Space Averaging and Time Averaging on Weather Forecast Skill
Authors: Ying Li, Samuel N. Stechmann
First page: 949
Abstract: Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern forecasts, and it is therefore not clear how much improvement in forecast performance is produced via averaging. Here we present a direct investigation of averaging effects, based on data from operational numerical weather forecasts. Data is analyzed for precipitation and surface temperature, for lead times of roughly 1 to 7 days, and for time- and space-averaging diameters of 1 to 7 days and 100 to 4500 km, respectively. For different geographic locations, the effects of time- or space-averaging can be different, and while no clear geographical pattern is seen for precipitation, a clear spatial pattern is seen for temperature. For temperature, in general, time averaging is most effective near coastlines, also effective over land, and least effective over oceans. Based on all locations globally, time averaging was less effective than one might expect. To help understand why time averaging may sometimes be minimally effective, a stochastic model is analyzed as a synthetic weather time series, and analytical formulas are presented for the decorrelation time. In effect, while time averaging creates a time series that is visually smoother, it does not necessarily cause a substantial increase in the predictability of the time series.
Citation: Forecasting
PubDate: 2022-11-24
DOI: 10.3390/forecast4040052
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 969-1003: The Lasso and the Factor
Zoo-Predicting Expected Returns in the Cross-Section
Authors: Marcial Messmer, Francesco Audrino
First page: 969
Abstract: We investigate whether Lasso-type linear methods are able to improve the predictive accuracy of OLS in selecting relevant firm characteristics for forecasting the future cross-section of stock returns. Through extensive Monte Carlo simulations, we show that Lasso-type predictions are superior to OLS when type II errors are a concern. The results change if the aim is to minimize type I errors. Finally, we analyze the predictive performance of the competing methods on the US cross-section of stock returns between 1974 and 2020 and show that only small and micro-cap stocks are highly predictable throughout the entire sample.
Citation: Forecasting
PubDate: 2022-11-25
DOI: 10.3390/forecast4040053
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 1004-1018: A Coordinated Analysis of Physical
Reactivity to Daily Stressors: Age and Proactive Coping Matter
Authors: Shevaun D. Neupert, Emily L. Smith, Margaret L. Schriefer
First page: 1004
Abstract: Proactive coping involves efforts to prepare for future stressors and may have implications for physical responses to stress. We examined age differences in physical reactivity to daily stressors moderated by proactive coping in a coordinated analysis across two separate daily diary studies. Study 1 included data from 116 older (age range 60–90) and 107 younger (age range 18–36) adults on daily stressors and physical health symptoms for 8 consecutive days. Study 2 included data from 140 adults (age range 19–86) on daily stressors and self-rated physical health for 29 consecutive days. Participants in both studies reported on their proactive coping on the first day of the study. Physical reactivity was indexed via lagged multilevel models as increases in daily physical symptoms in Study 1 and decreases in daily physical health in Study 2 with corresponding increases in daily stressors. Results indicated that in both studies, younger adults with low proactive coping were more physically reactive to daily stressors compared to younger adults with high proactive coping. Proactive coping was associated with reduced physical reactivity to daily stressors among younger adults, consistent with the characterization of a high degree of control and ample opportunities at earlier phases of adulthood which are critical for accumulating resources to proactively cope.
Citation: Forecasting
PubDate: 2022-11-29
DOI: 10.3390/forecast4040054
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 1019-1037: The Contribution of Digital
Technology to the Forecasting of Supply Chain Development, in IT Products,
Modeling and Simulation of the Problem
Authors: Dimitrios K. Nasiopoulos, Dimitrios M. Mastrakoulis, Dimitrios A. Arvanitidis
First page: 1019
Abstract: Aiming for the forecasting and predictability of their future development, corporations have developed appropriate strategies as a result of the necessity to optimize the distribution networks of new IT products over time. The necessity of diversifying manufacturing was brought on by the fierce competition between businesses and the sophisticated consumer demand trends for personalized items. For businesses looking to create more effective distribution networks for their products, mass adaptability may be advantageous. Fuzzy cognitive mapping (FCM), associations developed from web analytics data, and simulation results based on dynamic and agent-based simulation models work together to practically aid digital marketing experts, decision-makers and analysts in offering answers to their corresponding problems. In order to apply the measures in agent-based modeling, the current work is based on the gathering of web analysis data over a predetermined time period, as well as on identifying the influence correlations between measurements.
Citation: Forecasting
PubDate: 2022-11-29
DOI: 10.3390/forecast4040055
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 1038-1050: Modeling and Forecasting Somali
Economic Growth Using ARIMA Models
Authors: Abas Omar Mohamed
First page: 1038
Abstract: The study investigated the empirical role of past values of Somalia’s GDP growth rates in its future realizations. Using the Box–Jenkins modeling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample Somali GDP growth rates for fourteen quarters. Balancing between parsimony and fitness criteria of model selection, the study found Autoregressive Integrated Moving Average ARIMA (5,1,2) to be the most appropriate model to estimate and forecast the trajectory of Somali economic growth. The study sourced the GDP growth data from World Bank World Development Indicators (WDI) for the period between 1960 to 2022. The study results predict that Somalia’s GDP will, on average, experience 4 percent quarterly growth rates for the coming three and half years. To solidify the validity of the forecasting results, the study conducted several ARIMA and rolling window diagnostic tests. The model errors proved to be white noise, the moving average (MA) and Autoregressive (AR) components are covariances stationary, and the rolling window test shows model stability within a 95% confidence interval. These optimistic economic growth forecasts represent a policy dividend for the government of Somalia after almost a decade-long stick-and-carrot economic policies between strict IMF fiscal disciplinary measures and World Bank development investments on target projects. The study, however, acknowledges that the developments of current severe droughts, locust infestations, COVID-19 pandemic, internal political, and security stability, and that the active involvement of international development partners will play a crucial role in the realization of these promising growth projections.
Citation: Forecasting
PubDate: 2022-11-30
DOI: 10.3390/forecast4040056
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 1051-1079: Ecological Forecasting and
Operational Information Systems Support Sustainable Ocean Management
Authors: Chaojiao Sun, Alistair J. Hobday, Scott A. Condie, Mark E. Baird, J. Paige Eveson, Jason R. Hartog, Anthony J. Richardson, Andrew D. L. Steven, Karen Wild-Allen, Russell C. Babcock, Dezhou Yang, Rencheng Yu, Mathieu Mongin
First page: 1051
Abstract: In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due to environmental forcing such as climate variability and change, extreme weather conditions, pollution, or habitat change. Here, we summarise examples from several sectors and a range of locations. We describe the need, approach, forecast performance, delivery system, and end user uptake. This examination shows that near-term ecological forecasts are needed by end users, decisions are being made based on forecasts, and there is an urgent need to develop operational information systems to support sustainable ocean management. An operational information system is critical for connecting to decision makers and providing an enduring approach to forecasting and proactive decision making. These operational systems require significant investment and ongoing maintenance but are key to delivering ecological forecasts for societal benefits. Iterative forecasting practices could provide continuous improvement by incorporating evaluation and feedback to overcome the limitations of the imperfect model and incomplete observations to achieve better forecast outcomes and accuracy.
Citation: Forecasting
PubDate: 2022-12-16
DOI: 10.3390/forecast4040057
Issue No: Vol. 4, No. 4 (2022)
- Forecasting, Vol. 4, Pages 582-603: Assessing the Implication of Climate
Change to Forecast Future Flood Using CMIP6 Climate Projections and
HEC-RAS Modeling
Authors: Abhiru Aryal, Albira Acharya, Ajay Kalra
First page: 582
Abstract: Climate change has caused uncertainty in the hydrological pattern including weather change, precipitation fluctuations, and extreme temperature, thus triggering unforeseen natural tragedies such as hurricanes, flash flooding, heatwave and more. Because of these unanticipated events occurring all around the globe, the study of the influence of climate change on the alteration of flooding patterns has gained a lot of attention. This research study intends to provide an insight into how the future projected streamflow will affect the flooding-inundation extent by comparing the change in floodplain using both historical and future simulated scenarios. For the future projected data, the climate model Atmosphere/Ocean General Circulation Model (AOGCM) developed by Coupled Model Intercomparison Project Phase 6 (CMIP6) is used, which illustrates that the flood is increasing in considering climate models. Furthermore, a comparison of the existing flood inundation map by the Federal Emergency Management Agency (FEMA) study with the map generated by future projected streamflow data presents the entire inundation area in flood maps, implying the expansion area compared to FEMA needs to be considered in making emergency response plans. The effect of flooding in the inundation area from historical to future flow values, presented mathematically by a calculation of inundation extent percentage, infers that the considered watershed of Rock River is a flood-prone area. The goal is to provide insights on the importance of using the forecasted data for flood analysis and to offer the necessary background needed to strategize an emergency response plan for flood management.
Citation: Forecasting
PubDate: 2022-06-29
DOI: 10.3390/forecast4030032
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 604-633: Integrating Ecological Forecasting
into Undergraduate Ecology Curricula with an R Shiny Application-Based
Teaching Module
Authors: Tadhg N. Moore, R. Quinn Thomas, Whitney M. Woelmer, Cayelan C. Carey
First page: 604
Abstract: Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.
Citation: Forecasting
PubDate: 2022-06-30
DOI: 10.3390/forecast4030033
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 634-653: Influence of Car Configurator Webpage
Data from Automotive Manufacturers on Car Sales by Means of Correlation
and Forecasting
Authors: Juan Manuel García Sánchez, Xavier Vilasís Cardona, Alexandre Lerma Martín
First page: 634
Abstract: A methodology to prove the influence of car configurator webpage data for automotive manufacturers is developed across this research. Firstly, the correlation between online data and sales is measured. Afterward, car variant sales are predicted using a set of forecasting techniques divided into univariate and multivariate ones. Finally, weekly color mix sales based on these techniques are built and compared. Results show that users visit car configurator webpages 1 to 6 months before the purchase date. Additionally, car variants predictions and weekly color mix sales derived from multivariate techniques, i.e., using car configurator data as external input, provide improvement up to 25 points in the assessment metric.
Citation: Forecasting
PubDate: 2022-07-11
DOI: 10.3390/forecast4030034
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 654-673: Modelling Financial Markets during
Times of Extreme Volatility: Evidence from the GameStop Short Squeeze
Authors: Boris Andreev, Georgios Sermpinis, Charalampos Stasinakis
First page: 654
Abstract: Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests.
Citation: Forecasting
PubDate: 2022-07-19
DOI: 10.3390/forecast4030035
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 674-684: The Power of Travel Search Data in
Forecasting the Tourism Demand in Dubai
Authors: Ahmed Shoukry Rashad
First page: 674
Abstract: Tourism plays an important economic role for many economies and after the COVID-19 pandemic, accurate tourism forecasting become critical for policymakers in tourism-dependent economies. This paper extends the growing literature on the use of internet search data in tourism forecasting through evaluating the predictive ability of Destination Insight with Google, a new Google product designed to monitor tourism recovery after the COVID-19 pandemic. This paper is the first attempt to explore the forecasting ability of the new Google data. The study focuses on the case of Dubai, given its status as a world-leading tourism destination. The study uses time series models that account for seasonality, trending variables, and structural breaks. The study uses monthly data for the period of January 2019 to April 2022. We explore whether the internet travel search queries can improve the forecasting of tourist arrivals to Dubai from the UK. We evaluate the accuracy of forecasts after incorporating the Google variable in our model. Our findings suggest that the new Google data can significantly improve tourism forecasting and serves as a leading indicator of tourism demand.
Citation: Forecasting
PubDate: 2022-07-21
DOI: 10.3390/forecast4030036
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 685-698: Examining Factors That Affect Movie
Gross Using Gaussian Copula Marginal Regression
Authors: Joshua Eklund, Jong-Min Kim
First page: 685
Abstract: In this research, we investigate the relationship between a movie’s gross and its budget, year of release, season of release, genre, and rating. The movie data used in this research are severely skewed to the right, resulting in the problems of nonlinearity, non-normal distribution, and non-constant variance of the error terms. To overcome these difficulties, we employ a Gaussian copula marginal regression (GCMR) model after adjusting the gross and budget variables for inflation using a consumer price index. An analysis of the data found that year of release, budget, season of release, genre, and rating were all statistically significant predictors of movie gross. Specifically, one unit increases in budget and year were associated with an increase in movie gross. G movies were found to gross more than all other kinds of movies (PG, PG-13, R, and Other). Movies released in the fall were found to gross the least compared to the other three seasons. Finally, action movies were found to gross more than biography, comedy, crime, and other movie genres, but gross less than adventure, animation, drama, fantasy, horror, and mystery movies.
Citation: Forecasting
PubDate: 2022-07-21
DOI: 10.3390/forecast4030037
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 699-716: Can Groups Improve Expert Economic and
Financial Forecasts'
Authors: Warwick Smith, Anca M. Hanea, Mark A. Burgman
First page: 699
Abstract: Economic and financial forecasts are important for business planning and government policy but are notoriously challenging. We take advantage of recent advances in individual and group judgement, and a data set of economic and financial forecasts compiled over 25 years, consisting of multiple individual and institutional estimates, to test the claim that nominal groups will make more accurate economic and financial forecast than individuals. We validate the forecasts using the subsequent published (real) outcomes, explore the performance of nominal groups against institutions, identify potential superforecasters and discuss the benefits of implementing structured judgment techniques to improve economic and financial forecasts.
Citation: Forecasting
PubDate: 2022-08-02
DOI: 10.3390/forecast4030038
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 717-731: Nowcasting GDP: An Application to
Portugal
Authors: João B. Assunção, Pedro Afonso Fernandes
First page: 717
Abstract: Forecasting the state of an economy is important for policy makers and business leaders. When this is conducted in real-time, it is called nowcasting. In this paper, we present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available. When the economic environment changes fast, as has happened often in the last decades across most developed economies, it is important to use forecasting methods that are both flexible and robust. This can be achieved with bridge equations and non-parametric estimates of the trend growth using only publicly available information. The method presented in this paper achieves, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities.
Citation: Forecasting
PubDate: 2022-08-15
DOI: 10.3390/forecast4030039
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 732-751: Evaluating State-of-the-Art,
Forecasting Ensembles and Meta-Learning Strategies for Model Fusion
Authors: Pieter Cawood, Terence Van Zyl
First page: 732
Abstract: The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
Citation: Forecasting
PubDate: 2022-08-18
DOI: 10.3390/forecast4030040
Issue No: Vol. 4, No. 3 (2022)
- Forecasting, Vol. 4, Pages 409-419: A Monte Carlo Approach to Bitcoin
Price Prediction with Fractional Ornstein–Uhlenbeck Lévy
Process
Authors: Jules Clément Mba, Sutene Mwambetania Mwambi, Edson Pindza
First page: 409
Abstract: Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.
Citation: Forecasting
PubDate: 2022-03-30
DOI: 10.3390/forecast4020023
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 420-437: Forecasting Regional Tourism Demand in
Morocco from Traditional and AI-Based Methods to Ensemble Modeling
Authors: El houssin Ouassou, Hafsa Taya
First page: 420
Abstract: Tourism is one of the main sources of wealth for the Moroccan regions, since, in 2019, it contributed 7.1% to the total GDP. However, it is considered to be one of the sectors most vulnerable to exogenous shocks (political and social stability, currency change, natural disasters, pandemics, etc.). To control this, policymakers tend to use various techniques to forecast tourism demand for making crucial decisions. In this study, we aimed to forecast the number of tourist arrivals to the Marrakech-Safi region using annual data for the period from 1999 to 2018 by using three conventional approaches (ARIMA, AR, and linear regression), and then we compared the results with three artificial intelligence-based techniques (SVR, XGBoost, and LSTM). Then, we developed hybrid models by combining both the conventional and AI-based models, using the technique of ensemble learning. The findings indicated that the hybrid models outperformed both conventional and AI-based techniques. It is clear from the results that using hybrid models can overcome the limitations of each method individually.
Citation: Forecasting
PubDate: 2022-04-06
DOI: 10.3390/forecast4020024
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 438-455: Modelling Energy Transition in
Germany: An Analysis through Ordinary Differential Equations and System
Dynamics
Authors: Andrea Savio, Luigi De Giovanni, Mariangela Guidolin
First page: 438
Abstract: This paper proposes the application of a multivariate diffusion model, based on ordinary differential equations, to investigate the energy transition in Germany. Specifically, the model is able to analyze the dynamic interdependencies between coal, gas and renewables in the energy market. A system dynamics representation of the model is also performed, allowing a deeper understanding of the system and the set-up of suitable strategic interventions through a simulation exercise. Such simulation provides a useful indication of how renewable energy consumption may be stimulated as a result of well-specified policies.
Citation: Forecasting
PubDate: 2022-04-08
DOI: 10.3390/forecast4020025
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 456-476: Diffusion of Solar PV Energy in the
UK: A Comparison of Sectoral Patterns
Authors: Anita M. Bunea, Mariangela Guidolin, Piero Manfredi, Pompeo Della Della Posta
First page: 456
Abstract: The paper applies innovation diffusion models to study the adoption process of solar PV energy in the UK from 2010 to 2021 by comparing the trajectories between three main categories, residential, commercial, and utility, in terms of both the number of installations and installed capacity data. The effect of the UK incentives on adoptions by those categories is studied by analyzing the timing, intensity, and persistence of the perturbations on adoption curves. The analysis confirms previous findings on PV adoption, namely the fragile role of the media support to solar PV, the ability of the proposed model to capture both the general trend of adoptions and the effects induced by ad hoc incentives, and the dramatic dependence of solar PV from public incentives. Thanks to the granularity of the data, the results reveal several interesting aspects, related both to differences in adoption patterns depending on the category considered, and to some regularities across categories. A comparison between the models for number of installations and for installed capacity data suggests that the latter (usually more easily available than the former) may be highly informative and, in some cases, may provide a reliable description of true adoption data.
Citation: Forecasting
PubDate: 2022-04-20
DOI: 10.3390/forecast4020026
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 477-502: Monitoring and Forecasting of Key
Functions and Technologies for Automated Driving
Authors: Christian Ulrich, Benjamin Frieske, Stephan A. Schmid, Horst E. Friedrich
First page: 477
Abstract: Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods.
Citation: Forecasting
PubDate: 2022-05-20
DOI: 10.3390/forecast4020027
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 501-524: Advances in Time Series Forecasting
Development for Power Systems’ Operation with MLOps
Authors: Gonca Gürses-Tran, Antonello Monti
First page: 501
Abstract: Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet.
Citation: Forecasting
PubDate: 2022-05-26
DOI: 10.3390/forecast4020028
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 525-537: Estimating Path Choice Models through
Floating Car Data
Authors: Antonio Comi, Antonio Polimeni
First page: 525
Abstract: The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts.
Citation: Forecasting
PubDate: 2022-06-03
DOI: 10.3390/forecast4020029
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 538-564: Analyzing and Forecasting
Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme
Learning Machine Hybridization Approach
Authors: Emmanuel Senyo Fianu
First page: 538
Abstract: Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.
Citation: Forecasting
PubDate: 2022-06-11
DOI: 10.3390/forecast4020030
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 565-581: Deep Learning for Demand Forecasting
in the Fashion and Apparel Retail Industry
Authors: Chandadevi Giri, Yan Chen
First page: 565
Abstract: Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested items is promising, and this model could be effectively used to solve forecasting problems.
Citation: Forecasting
PubDate: 2022-06-20
DOI: 10.3390/forecast4020031
Issue No: Vol. 4, No. 2 (2022)
- Forecasting, Vol. 4, Pages 72-94: SIMLR: Machine Learning inside the SIR
Model for COVID-19 Forecasting
Authors: Roberto Vega, Leonardo Flores, Russell Greiner
First page: 72
Abstract: Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.
Citation: Forecasting
PubDate: 2022-01-13
DOI: 10.3390/forecast4010005
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 95-125: Analysing Historical and Modelling
Future Soil Temperature at Kuujjuaq, Quebec (Canada): Implications on
Aviation Infrastructure
Authors: Andrew C. W. Leung, William A. Gough, Tanzina Mohsin
First page: 95
Abstract: The impact of climate change on soil temperatures at Kuujjuaq, Quebec in northern Canada is assessed. First, long-term historical soil temperature records (1967–1995) are statistically analyzed to provide a climatological baseline for soils at 5 to 150 cm depths. Next, the nature of the relationship between atmospheric variables and soil temperature are determined using a statistical downscaling model (SDSM) and National Centers for Environmental Prediction (NCEP), a climatological data set. SDSM was found to replicate historic soil temperatures well and used to project soil temperatures for the remainder of the century using climate model output Canadian Second Generation Earth System Model (CanESM2). Three Representative Concentration Pathway scenarios (RCP 2.6, 4.5 and 8.5) were used from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This study found that the soil temperature at this location may warm at 0.9 to 1.2 °C per decade at various depths. Annual soil temperatures at all depths are projected to rise to above 0 °C for the 1997–2026 period for all climate scenarios. The melting soil poses a hazard to the airport infrastructure and will require adaptation measures.
Citation: Forecasting
PubDate: 2022-01-13
DOI: 10.3390/forecast4010006
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 126-148: Hybrid Surrogate Model for Timely
Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow
Authors: Andre D. L. Zanchetta, Paulin Coulibaly
First page: 126
Abstract: Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime.
Citation: Forecasting
PubDate: 2022-01-23
DOI: 10.3390/forecast4010007
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 149-164: Short Term Electric Power Load
Forecasting Using Principal Component Analysis and Recurrent Neural
Networks
Authors: Venkataramana Veeramsetty, Dongari Rakesh Chandra, Francesco Grimaccia, Marco Mussetta
First page: 149
Abstract: Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.
Citation: Forecasting
PubDate: 2022-01-24
DOI: 10.3390/forecast4010008
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 165-181: Trend Lines and Japanese Candlesticks
Applied to the Forecasting of Wind Speed Data Series
Authors: Manfredo Guilizzoni, Paloma Maldonado Eizaguirre
First page: 165
Abstract: One of the most critical issues for wind energy exploitation is the high variability of the resource, resulting in very difficult forecasting of the power that wind farms can grant. A vast literature has therefore been devoted to wind speed and wind power quantitative forecasting, using different techniques. The widely used statistical and learning models that are based on a continuation in the future of the series’ past behaviour offer a performance that may be much less satisfactory when the values suddenly change their trend. The application to wind speed data of two techniques usually employed for the technical analysis of financial series–namely support and resistances identification and candlestick charts–is investigated here, with the main aim to detect inversion points in the series. They are applied to wind speed data series for two locations in Spain and Italy. The proposed indicators confirm their usefulness in identifying peculiar behaviours in the system and conditions where the trend may be expected to change. This additional information offered to the forecasting algorithms may also be included in innovative approaches, e.g., based on machine learning.
Citation: Forecasting
PubDate: 2022-01-27
DOI: 10.3390/forecast4010009
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 182-183: Acknowledgment to the Reviewers of
Forecasting in 2021
Authors: Forecasting Editorial Office Forecasting Editorial Office
First page: 182
Abstract: Rigorous peer reviews are the basis of high-quality academic publishing [...]
Citation: Forecasting
PubDate: 2022-01-29
DOI: 10.3390/forecast4010010
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 184-207: A Hybrid XGBoost-MLP Model for Credit
Risk Assessment on Digital Supply Chain Finance
Authors: Yixuan Li, Charalampos Stasinakis, Wee Meng Yeo
First page: 184
Abstract: Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF). In this paper, 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 are selected as the empirical sample, and the important features of credit risk assessment in DSCF are automatically selected through the feature selection of the XGBoost model in the first stage, then followed by credit risk assessment through the MLP in the second stage. Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model. From the perspective of DSCF, the results show that the inclusion of digital features improves the accuracy of credit risk assessment in SCF.
Citation: Forecasting
PubDate: 2022-01-29
DOI: 10.3390/forecast4010011
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 208-218: Projecting Mortality Rates to Extreme
Old Age with the CBDX Model
Authors: Kevin Dowd, David Blake
First page: 208
Abstract: We introduce a simple extension to the CBDX model to project cohort mortality rates to extreme old age. The proposed approach fits a polynomial to a sample of age effects, uses the fitted polynomial to project the age effects to ages beyond the sample age range, then splices the sample and projected age effects, and uses the spliced age effects to obtain mortality rates for the higher ages. The proposed approach can be used to value financial instruments such as life annuities that depend on projections of extreme old age mortality rates.
Citation: Forecasting
PubDate: 2022-02-02
DOI: 10.3390/forecast4010012
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 219-237: Side-Length-Independent Motif (SLIM):
Motif Discovery and Volatility Analysis in Time Series—SAX, MDL and
the Matrix Profile
Authors: Eoin Cartwright, Martin Crane, Heather J. Ruskin
First page: 219
Abstract: As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length do so over a given range, with the restriction that both sides of an identified sub-sequence pair are of equal length. In this article, motivated by a better localised representation of variations in time series, a novel approach to the identification of motifs is discussed, which allows for some flexibility in side-length. The advantages of this flexibility include improved recognition of localised similar behaviour (manifested as motif shape) over varying timescales. As well as facilitating improved interpretation of localised volatility patterns and a visual comparison of relative volatility levels of series at a globalised level. The process described extends and modifies established techniques, namely SAX, MDL and the Matrix Profile, allowing advantageous properties of leading algorithms for data analysis and dimensionality reduction to be incorporated and future-proofed. Although this technique is potentially applicable to any time series analysis, the focus here is financial and energy sector applications where real-world examples examining S&P500 and Open Power System Data are also provided for illustration.
Citation: Forecasting
PubDate: 2022-02-04
DOI: 10.3390/forecast4010013
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 238-261: High-Resolution Gridded Air
Temperature Data for the Urban Environment: The Milan Data Set
Authors: Giuseppe Frustaci, Samantha Pilati, Cristina Lavecchia, Enea Marco Montoli
First page: 238
Abstract: Temperature is the most used meteorological variable for a large number of applications in urban resilience planning, but direct measurements using traditional sensors are not affordable at the usually required spatial density. On the other hand, spaceborne remote sensing provides surface temperatures at medium to high spatial resolutions, almost compatible with the needed requirements. However, in this case, limitations are represented by cloud conditions and passing times together with the fact that surface temperature is not directly comparable to air temperature. Various methodologies are possible to take benefits from both measurements and analysis methods, such as direct assimilation in numerical models, multivariate analysis, or statistical interpolation. High-resolution thermal fields in the urban environment are also obtained by numerical modelling. Several codes have been developed to resolve at some level or to parameterize the complex urban boundary layer and are used for research and applications. Downscaling techniques from global or regional models offer another possibility. In the Milan metropolitan area, given the availability of both a high-quality urban meteorological network and spaceborne land surface temperatures, and also modelling and downscaling products, these methods can be directly compared. In this paper, the comparison is performed using: the ClimaMi Project high-quality data set with the accurately selected measurements in the Milan urban canopy layer, interpolated by a cokriging technique with remote-sensed land surface temperatures to enhance spatial resolution; the UrbClim downscaled data from the reanalysis data set ERA5; a set of near-surface temperatures produced by some WRF outputs with the building environment parameterization urban scheme. The comparison with UrbClim and WRF of the cokriging interpolated data set, mainly based on the urban canopy layer measurements and covering several years, is presented and discussed in this article. This comparison emphasizes the primary relevance of surface urban measurements and highlights discrepancies with the urban modelling data sets.
Citation: Forecasting
PubDate: 2022-02-08
DOI: 10.3390/forecast4010014
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 262-274: Explainable Ensemble Machine Learning
for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features
Authors: Alireza Rezazadeh, Yasamin Jafarian, Ali Kord
First page: 262
Abstract: Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.
Citation: Forecasting
PubDate: 2022-02-13
DOI: 10.3390/forecast4010015
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 275-306: Switching Coefficients or Automatic
Variable Selection: An Application in Forecasting Commodity Returns
Authors: Massimo Guidolin, Manuela Pedio
First page: 275
Abstract: In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts.
Citation: Forecasting
PubDate: 2022-02-18
DOI: 10.3390/forecast4010016
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 307-334: Do Risky Scenarios Affect Forecasts of
Savings and Expenses'
Authors: Shari De Baets, Dilek Önkal, Wasim Ahmed
First page: 307
Abstract: Many people do not possess the necessary savings to deal with unexpected financial events. People’s biases play a significant role in their ability to forecast future financial shocks: they are typically overoptimistic, present-oriented, and generally underestimate future expenses. The purpose of this study is to investigate how varying risk information influences people’s financial awareness, in order to reduce the chance of a financial downfall. Specifically, we contribute to the literature by exploring the concept of ‘nudging’ and its value for behavioural changes in personal financial management. While of great practical importance, the role of nudging in behavioural financial forecasting research is scarce. Additionally, the study steers away from the standard default choice architecture nudge, and adds originality by focusing on eliciting implementation intentions and precommitment strategies as types of nudges. Our experimental scenarios examined how people change their financial projections in response to nudges in the form of new information on relevant risks. Participants were asked to forecast future expenses and future savings. They then received information on potential events identified as high-risk, low-risk or no-risk. We investigated whether they adjusted their predictions in response to various risk scenarios or not and how such potential adjustments were affected by the information given. Our findings suggest that the provision of risk information alters financial forecasting behaviour. Notably, we found an adjustment effect even in the no-risk category, suggesting that governments and institutions concerned with financial behaviour can increase financial awareness merely by increasing salience about possible financial risks. Another practical implication relates to splitting savings into different categories, and by using different wordings: A financial advisory institution can help people in their financial behaviour by focusing on ‘targets’, and by encouraging (nudging) people to make breakdown forecasts rather than general ones.
Citation: Forecasting
PubDate: 2022-02-21
DOI: 10.3390/forecast4010017
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 335-337: Editorial for Special Issue:
“Feature Papers of Forecasting 2021”
Authors: Sonia Leva
First page: 335
Abstract: The human capability to react or adapt to upcoming changes strongly relies on the ability to forecast them [...]
Citation: Forecasting
PubDate: 2022-03-03
DOI: 10.3390/forecast4010018
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 338-348: Irradiance Nowcasting by Means of
Deep-Learning Analysis of Infrared Images
Authors: Alessandro Niccolai, Seyedamir Orooji, Andrea Matteri, Emanuele Ogliari, Sonia Leva
First page: 338
Abstract: This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.
Citation: Forecasting
PubDate: 2022-03-04
DOI: 10.3390/forecast4010019
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 349-370: Application of Agent-Based Modeling in
Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia
Authors: Sardorbek Musayev, Jonathan Mellor, Tara Walsh, Emmanouil Anagnostou
First page: 349
Abstract: Effective weather forecast information helps smallholder farmers improve their adaptation to climate uncertainties and crop productivity. The main objective of this study was to assess the impact of weather forecast adoption on crop productivity. We coupled agent-based and crop productivity models to study the impact of farmers’ management decisions on maize productivity under different rainfall scenarios in Ethiopia. A household survey was conducted with 100 households from 5 villages and was used to validate the crop model. The agent-based model (ABM) analyzed the farmers’ behaviors in crop management under different dry, wet, and normal rainfall conditions. ABM results and crop data from the survey were then used as input data sources for the crop model. Our results show that farming decisions based on weather forecast information improved yield productivity from 17% to 30% under dry and wet seasons, respectively. The impact of adoption rates due to farmers’ intervillage interactions, connections, radio, agriculture extension services, and forecast accuracy brought additional crop yields into the Kebele compared to non-forecast users. Our findings help local policy makers to understand the impact of the forecast information. Results of this study can be used to develop agricultural programs where rainfed agriculture is common.
Citation: Forecasting
PubDate: 2022-03-13
DOI: 10.3390/forecast4010020
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 371-393: Prevalence and Economic Costs of
Absenteeism in an Aging Population—A Quasi-Stochastic Projection for
Germany
Authors: Patrizio Vanella, Christina Benita Wilke, Doris Söhnlein
First page: 371
Abstract: Demographic change is leading to the aging of German society. As long as the baby boom cohorts are still of working age, the working population will also age—and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend toward increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected to be close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/baby bust phenomenon determines demographic development in Germany in the midterm.
Citation: Forecasting
PubDate: 2022-03-15
DOI: 10.3390/forecast4010021
Issue No: Vol. 4, No. 1 (2022)
- Forecasting, Vol. 4, Pages 394-408: Machine-Learning-Based Functional Time
Series Forecasting: Application to Age-Specific Mortality Rates
Authors: Ufuk Beyaztas, Hanlin Shang
First page: 394
Abstract: We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional principal components and their associated principal component scores. Machine-learning-based multi-step-ahead forecasting strategies, which automatically learn the underlying structure of the data, are used to obtain the future realization of the principal component scores. The forecasted mortality curves are obtained by combining the dynamic functional principal components and forecasted principal component scores. The point and interval forecast accuracy of the proposed method is evaluated using six age-specific mortality datasets and compared favorably with four existing functional time series methods.
Citation: Forecasting
PubDate: 2022-03-18
DOI: 10.3390/forecast4010022
Issue No: Vol. 4, No. 1 (2022)
