An ARMA type fuzzy time series forecasting method based on particle swarm optimization
type: Item
instance of: scholarly article
title: An ARMA type fuzzy time series forecasting method based on particle swarm optimization
zbMATH Open document ID: 1299.62088
DOI: 10.1155/2013/935815
author: Erol Egrioglu
author: Ufuk Yolcu
author: C. H. Aladag
author: Cem Kocak
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: In the literature, fuzzy time series forecasting models generally include fuzzy lagged variables. Thus, these fuzzy time series models have only autoregressive structure. Using such fuzzy time series models can cause modeling error and bad forecasting performance like in conventional time series analysis. To overcome these problems, a new first-order fuzzy time series which forecasting approach including both autoregressive and moving average structures is proposed in this study. Also, the proposed model is a time invariant model and based on particle swarm optimization heuristic. To show the applicability of the proposed approach, some methods were applied to five time series which were also forecasted using the proposed method. Then, the obtained results were compared to those obtained from other methods available in the literature. It was observed that the most accurate forecast was obtained when the proposed approach was employed.
Mathematics Subject Classification ID: 62M86
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6373309
Wikidata QID: Q59029740
describes a project that uses: longmemo
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/935815
OpenAlex ID: W2133010150
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description: scientific article; zbMATH DE number 6373309