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PROBABILITIES AND MATH STATISTICS (113 journals)                     

Showing 1 - 98 of 98 Journals sorted alphabetically
Advances in Statistics     Open Access   (Followers: 10)
Afrika Statistika     Open Access   (Followers: 1)
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 11)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Data Science     Hybrid Journal   (Followers: 14)
Annual Review of Statistics and Its Application     Full-text available via subscription   (Followers: 7)
Applied Medical Informatics     Open Access   (Followers: 11)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Asian Journal of Probability and Statistics     Open Access  
Austrian Journal of Statistics     Open Access   (Followers: 4)
Biostatistics & Epidemiology     Hybrid Journal   (Followers: 4)
Cadernos do IME : Série Estatística     Open Access  
Calcutta Statistical Association Bulletin     Hybrid Journal  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 4)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics: Case Studies, Data Analysis and Applications     Hybrid Journal  
Comunicaciones en Estadística     Open Access  
Econometrics and Statistics     Hybrid Journal   (Followers: 1)
Forecasting     Open Access   (Followers: 1)
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 3)
Frontiers in Applied Mathematics and Statistics     Open Access   (Followers: 1)
Game Theory     Open Access   (Followers: 2)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 14)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 13)
Indonesian Journal of Applied Statistics     Open Access  
International Game Theory Review     Hybrid Journal   (Followers: 1)
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
International Journal of Advanced Statistics and Probability     Open Access   (Followers: 7)
International Journal of Algebra and Statistics     Open Access   (Followers: 3)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 3)
International Journal of Ecological Economics and Statistics     Full-text available via subscription   (Followers: 5)
International Journal of Energy and Statistics     Hybrid Journal   (Followers: 3)
International Journal of Game Theory     Hybrid Journal   (Followers: 3)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
International Journal of Multivariate Data Analysis     Hybrid Journal  
International Journal of Probability and Statistics     Open Access   (Followers: 4)
International Journal of Statistics & Economics     Full-text available via subscription   (Followers: 6)
International Journal of Statistics and Applications     Open Access   (Followers: 2)
International Journal of Statistics and Probability     Open Access   (Followers: 3)
International Journal of Statistics in Medical Research     Hybrid Journal   (Followers: 5)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Iraqi Journal of Statistical Sciences     Open Access  
Japanese Journal of Statistics and Data Science     Hybrid Journal  
Journal of Biometrics & Biostatistics     Open Access   (Followers: 4)
Journal of Cost Analysis and Parametrics     Hybrid Journal   (Followers: 5)
Journal of Environmental Statistics     Open Access   (Followers: 4)
Journal of Game Theory     Open Access   (Followers: 1)
Journal of Mathematical Economics and Finance     Full-text available via subscription  
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Modern Applied Statistical Methods     Open Access   (Followers: 1)
Journal of Official Statistics     Open Access   (Followers: 2)
Journal of Quantitative Economics     Hybrid Journal  
Journal of Social and Economic Statistics     Open Access  
Journal of Statistical Theory and Practice     Hybrid Journal   (Followers: 2)
Journal of Statistics and Data Science Education     Open Access   (Followers: 2)
Journal of Survey Statistics and Methodology     Hybrid Journal   (Followers: 4)
Journal of the Indian Society for Probability and Statistics     Full-text available via subscription  
Jurnal Biometrika dan Kependudukan     Open Access  
Jurnal Ekonomi Kuantitatif Terapan     Open Access  
Jurnal Sains Matematika dan Statistika     Open Access  
Lietuvos Statistikos Darbai     Open Access  
Mathematics and Statistics     Open Access   (Followers: 2)
Methods, Data, Analyses     Open Access   (Followers: 1)
METRON     Hybrid Journal   (Followers: 1)
Nepalese Journal of Statistics     Open Access  
North American Actuarial Journal     Hybrid Journal   (Followers: 1)
Open Journal of Statistics     Open Access   (Followers: 3)
Open Mathematics, Statistics and Probability Journal     Open Access  
Pakistan Journal of Statistics and Operation Research     Open Access   (Followers: 1)
Physica A: Statistical Mechanics and its Applications     Hybrid Journal   (Followers: 6)
Probability, Uncertainty and Quantitative Risk     Open Access   (Followers: 2)
Ratio Mathematica     Open Access  
Research & Reviews : Journal of Statistics     Open Access   (Followers: 3)
Revista Brasileira de Biometria     Open Access  
Revista Colombiana de Estadística     Open Access  
RMS : Research in Mathematics & Statistics     Open Access  
Romanian Statistical Review     Open Access  
Sankhya B - Applied and Interdisciplinary Statistics     Hybrid Journal  
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 1)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 2)
Spatial Statistics     Hybrid Journal   (Followers: 2)
Sri Lankan Journal of Applied Statistics     Open Access  
Stat     Hybrid Journal   (Followers: 1)
Stata Journal     Full-text available via subscription   (Followers: 8)
Statistica     Open Access   (Followers: 6)
Statistical Analysis and Data Mining     Hybrid Journal   (Followers: 23)
Statistical Theory and Related Fields     Hybrid Journal  
Statistics and Public Policy     Open Access   (Followers: 4)
Statistics in Transition New Series : An International Journal of the Polish Statistical Association     Open Access  
Statistics Research Letters     Open Access   (Followers: 1)
Statistics, Optimization & Information Computing     Open Access   (Followers: 3)
Stats     Open Access  
Synthesis Lectures on Mathematics and Statistics     Full-text available via subscription   (Followers: 1)
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Theory of Probability and Mathematical Statistics     Full-text available via subscription   (Followers: 2)
Turkish Journal of Forecasting     Open Access   (Followers: 1)
VARIANSI : Journal of Statistics and Its application on Teaching and Research     Open Access  
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal  

           

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Forecasting
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2571-9394
Published by MDPI Homepage  [84 journals]
  • 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 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)
       
 
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