Sharif University of Technology Scientia Iranica 1026-3098 32 16 2026 09 01 A new artificial neural network approach for time series analysis 24173 10.24200/sci.2022.58046.5536 EN Najmeh Neshat Industrial Engineering, Department, Meybod University, Meybod Hashem Mahlooji Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. Murat Kaya Program of Industrial Engineering, Sabanci University, Turkey. Journal Article 2021 04 04 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. https://scientiairanica.sharif.edu/article_24173_c178a9175169a4607f33d6763420495e.pdf