Fuzzy Autoregressive Distributed Lag model-based forecasting

Eren M.

FUZZY SETS AND SYSTEMS, cilt.459, ss.82-94, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 459
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.fss.2022.06.003
  • Dergi Adı: FUZZY SETS AND SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.82-94
  • Anahtar Kelimeler: Fuzzy regression, Autoregressive, Distributed Lag, Energy consumption, Forecast, LINEAR-REGRESSION ANALYSIS, TIME-SERIES, CONSUMPTION ESTIMATION, NEURAL-NETWORKS, ARIMA MODEL, ALGORITHM, UNCERTAINTY
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This research aims to be guided decision-makers in future planning by estimating the tendency of data consistently. In this context, it is thought that the integration of the Autoregressive Distributed Lag-ARDL models, gathering the independent factors and their past effects as well as the past trend of the dependent variable, with fuzzy regression methods, would give more realistic results. To prove the correctness of this idea, the Fuzzy-ARDL method has been proposed and tested the superiority of the research on the projection of USAs' annual oil consumption data examined by researchers previously. For this purpose, raw data of crude oil import price, population, gross national domestic production (GDP) per capita, and oil production variables, previously compiled annually, have been considered independent variables. Then the proposed model has been benchmarked with the other promising models from the fuzzy regression literature. As a result, according to various Accuracy Measures values, it has been seen that the proposed model outperforms the other promising models.(c) 2022 Elsevier B.V. All rights reserved.