Publisher: DePauw University   (Total: 3 journals)   [Sort by number of followers]

Showing 1 - 3 of 3 Journals sorted alphabetically
J. of Analytic Divinity     Open Access  
J. of the Turkish Chemical Society, Section A : Chemistry     Open Access  
Turkish J. of Forecasting     Open Access   (Followers: 1)
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Turkish Journal of Forecasting
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2618-6594
Published by DePauw University Homepage  [3 journals]
  • Estimating CO2 Emission Time Series with Support Vector Machines
           Regression, Artificial Neural Networks, and Classic Time Series Analysis

    • Authors: Fatih ÇEMREK; Özge DEMİR
      Abstract: Artificial intelligence machine learning has become very popular in recent years. It offers the ability to combine machine learning theory with many analyses such as classification, prediction models, natural language processing. Carbon dioxide emission is defined as the release of carbon, often caused by human nature, into the atmosphere. In the 19th century, the industrial revolution took place and the use of coal-powered industrial vehicles increased the amount of carbon released into the atmosphere. These gases released into the atmosphere have brought climate problems in proportion to the increase in temperature. Because of climate problems, the sweet water source of the earth’s ice pack continues to melt and the sea level rises. Therefore, the amount of carbon dioxide emission (metric tons per person) Artificial Neural Networks (ANN), Support Vector Machines Regression (SVMR), estimated by Box-Jenkins technique based on time series analysis and estimated estimates compared to MSE (mean square error) between 1990-2018. The comparison found that the Artificial Neural Networks have better predictive results on the SVMR and Box-Jenkins technique on the performance benchmark.
      PubDate: Fri, 31 Dec 2021 00:00:00 +030
  • A Poisson-Regression, Support Vector Machine and Grey Prediction Based
           Combined Forecasting Model Proposal: A Case Study in Distribution Business

    • Authors: Fatih YİĞİT; Şakir ESNAF, Bahar YALÇIN KAVUŞ
      Abstract: Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand forecasting has a direct impact on the performance of a company. The goal of the study is to present a new two-stage combination model named Hybrid-2-Best, for accurate demand forecasting. The model combines three forecasting models in a single combined forecast. The Hybrid-2-Best model uses a two-stage algorithm to achieve better-performing forecasts. Case study showed that the proposed Hybrid-2-Best model performs the best forecast performance among other combination techniques and individual methods. Furthermore, GP integration in the first and second stages gives flexibility. Experimental results indicate that the proposed Hybrid-2-Best model is a promising alternative for sales demand forecasting. MAPE of the proposed model is 0,13. This is a good result and better than compared other models. Proposed model performed better than other compared models in MASE and MSE as well
      PubDate: Fri, 31 Dec 2021 00:00:00 +030
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