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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 935815, 12 pages
Research Article

An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization

1Department of Statistics, Ondokuz Mayıs University, 55139 Samsun, Turkey
2Department of Statistics, Ankara University, 06100 Ankara, Turkey
3Department of Statistics, Hacettepe University, 06100 Ankara, Turkey
4Medical High School, Hitit University, 19000 Çorum, Turkey

Received 18 April 2013; Revised 21 June 2013; Accepted 22 June 2013

Academic Editor: Ming Li

Copyright © 2013 Erol Egrioglu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.