An ARMA type fuzzy time series forecasting method based on particle swarm optimization
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Dosyalar
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
Attribution 3.0 Unported (CC BY 3.0)
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
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. © 2013 Erol Egrioglu et al.
Açıklama
Anahtar Kelimeler
[Belirlenecek]
Kaynak
Mathematical Problems in Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
2013
Sayı
Künye
Eğrioğlu, E., Yolcu, U., Aladağ, Ç. H., Koçak, C. (2013). An ARMA type fuzzy time series forecasting method based on particle swarm optimization. Mathematical Problems in Engineering, 2013.
