A new fuzzy inference system for time series forecasting

A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap

Please use simprogtsfis, other programs are used in it.

A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap

Authors: Yolcu, Ufuka; * | Bas, Erenb | Egrioglu, Erolb
Affiliations: [a] Department of Econometrics, Faculty of Economic and Administrative Sciences, Forecast Research Laboratory, Giresun University, Giresun, Turkey | [b] Department of Statistics, Faculty of Arts and Sciences, Forecast Research Laboratory, Giresun University, Giresun, Turkey

Correspondence: [*] Corresponding author. Ufuk Yolcu, Department of Econometrics, Faculty of Economic and Administrative Sciences, Forecast Research Laboratory, Giresun University, 28200 Giresun, Turkey. Tel.: [phone]; Fax: [phone]; E-mail: [e-mail].

Abstract: Recent years, fuzzy inference systems have been commonly used for time series forecasting. It is well known that fuzzy inference systems can produce good forecasting. Although fuzzy inference systems like adaptive network fuzzy inference system have been preferred by many of researchers, these systems have many of problems. If data set contains many explanatory variables, the number of rules will increase dramatically. Classical fuzzy inference systems need to estimate too many parameters for a reasonable forecasting performance. In this study, a new fuzzy inference system is proposed for time series forecasting. The proposed inference system uses fuzzy c-means method for clustering and pi-sigma neural network for fuzzy modelling. Moreover, the proposed system can generate probabilistic outputs (forecasts) under favour of subsampling block bootstrap method. The performance of the proposed method was investigated by using some data sets. It is understood that the proposed inference system can produce better forecast results.
Keywords: Fuzzy inference systems, fuzzy c-means, particle swarm optimization, subsampling block bootstrap, probabilistic forecasts

DOI: 10.3233/JIFS-17782
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2349-2358, 2018