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A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

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Abstract

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.

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Authors and Affiliations

  1. Department of Nursing, Faculty of Health Sciences, Hitit University, Çorum, Turkey

    Cem Kocak

  2. Department of Statistics, Faculty of Arts and Science, Giresun University, Giresun, Turkey

    Erol Egrioglu & Eren Bas

Authors
  1. Cem Kocak
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  2. Erol Egrioglu
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  3. Eren Bas
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Correspondence to Cem Kocak.

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Kocak, C., Egrioglu, E. & Bas, E. A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory. J Supercomput 77, 6178–6196 (2021). https://doi.org/10.1007/s11227-020-03503-8

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