Gated recurrent unit network-based fuzzy time series forecasting model

Abstract

Time series forecasting has lots of applications in various industries such as weather, business, retail and energy consumption forecasting. Accurate prediction in these applications is very important and also difficult task because of complexity and uncertainty of time series. Nowadays, using deep learning methods is a popular approach in time series forecasting and shows better performance than classical methods. However, in the literature, there are few studies which use deep learning methods in fuzzy time series (FTS) forecasting. In this study, we propose a novel FTS forecasting model which is based upon hybridization of Recurrent Neural Networks with FTS to deal with complexity and also uncertanity of these series. The proposed model utilizes Gated Recurrent Unit (GRU) to make prediction by using combination of membership values and also past value from original time series data as model input and produce real forecast value. Moreover, the proposed model can handle first order fuzzy relations as well as high order ones. In experiments, we have compared our model results with those of state-of-art methods by using two real world datasets; The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Nikkei Stock Average. The results indicate that our model outperforms or performs similar to other methods. The proposed model is also validated by using Covid-19 active case dataset and shows better performance than Long Short-term Memory (LSTM) networks.