A new artificial neural network approach for time series analysis

Document Type : Research Article

Authors

1 Industrial Engineering, Department, Meybod University, Meybod

2 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.

3 Program of Industrial Engineering, Sabanci University, Turkey.

10.24200/sci.2022.58046.5536

Abstract

Time series analysis and accurate forecasting of energy prices are critical for both policymakers and market participants. In the practical analysis of price time series, the coefficients play vital roles; however, their accurate estimation is a challenging issue, as they are affected by external factors. This study proposes a new modeling approach for Artificial Neural Networks (ANNs) models based on fuzzy logic. For this purpose, we reformulated an ANN model as a fuzzy Non-Linear Regression (NLR) model to capture the advantages of both fuzzy regression and ANN methodologies. This clear-box model can be applied not only to uncertain, ambiguous, and complex environments, but it is also capable of modeling nonlinear patterns. To illustrate the capability of the proposed approach, we report a case study of Liquefied Natural Gas (LNG) prices in Japan’s market (as one of the world’s largest natural gas importers). The results support that the performance of the proposed approach is acceptable; moreover, it can deal with uncertain and complex environments as a clear-box model.

Keywords

Main Subjects


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Volume 32, Issue 16
Transactions on Industrial Engineering
September and October 2025 Article ID:5536
  • Receive Date: 04 April 2021
  • Revise Date: 12 February 2022
  • Accept Date: 09 May 2022