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

Yolcu, Ufuk and Bas, Eren and Egrioglu, Erol (2018) A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap. Journal of Intelligent and Fuzzy Systems, 35 (2). pp. 2349-2358. ISSN 1064-1246

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Intelligent and Fuzzy Systems
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
ID Code:
139300
Deposited By:
Deposited On:
29 Nov 2019 11:25
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 05:50