A new deep intuitionistic fuzzy time series forecasting method based on long short?term memory
[ X ]
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory (LSTM) is one of the deep artificial neural networks. There have been a few fuzzy time series forecasting model based on LSTM in the literature. However, LSTM has not been used in an intuitionistic fuzzy time series (IFTS) forecasting method until now. In this paper, determining the fuzzy relations is made by using the LSTM artificial neural network and so, a new intuitionistic fuzzy time series forecasting method based on LSTM is proposed. In the proposed method, obtaining the membership and non-membership values is performed by using intuitionistic fuzzy c-means. Then, the inputs of the LSTM are merged membership and non-membership values by a minimum operator. In this way, lagged crisp values are inputs of the long short-term memory. So, the proposed method is a high-order IFTS model. The architecture of the LSTM artificial neural network includes multiple inputs and a single output. The proposed method and some other methods in the literature are applied to the Giresun Temperature data and the Nikkei 225 stock exchange time series, and the forecasting performance of these methods is compared.
Açıklama
Anahtar Kelimeler
Intuitionistic Fuzzy Time Series, Long Short-term Memory, Intuitionistic Fuzzy c-means, Deep Learning
Kaynak
The Journal of Supercomputing
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
77
Sayı
6
Künye
Kocak, C., Egrioglu, E., Bas, E. (2020). A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory. The Journal of Supercomputing, 1-19.
