Recurrent fuzzy time series functions approaches for forecasting

Egrioglu E., Fildes R., Baş E.

Granular Computing, vol.7, no.1, pp.163-170, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 7 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1007/s41066-021-00257-3
  • Journal Name: Granular Computing
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.163-170
  • Keywords: Forecasting, Fuzzy sets, Fuzzy time series functions, Bootstrap method, Recurrent inference systems, ALGORITHM
  • Karadeniz Technical University Affiliated: No


© 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.The recurrent fuzzy time series function method can be obtained in two ways. First is using a similar model to the autoregressive moving average to obtain fuzzy functions and the second one is using recurrent connections in the combining equation. The recurrent structure provides less number of inputs and more accurate forecasts. The contribution of the paper is proposing a recurrent fuzzy time series function method and its bootstrapped version. The recurrent models are used to obtain fuzzy functions in the proposed method. The classical bootstrap method is applied and bootstrap samples are generated from learning samples. The bootstrap method provides lower forecast error variance for the proposed method. The performance of new methods is compared with some fuzzy function methods and classical approaches. First, Turkey electrical consumption data time series are analyzed. Second, the Australian beer consumption time series are analyzed. As a result of applications, new methods have good forecasting performance if compare to established benchmarks.