Abstract:
Time series forecasting and prediction are utilized in various industries, such as e-commerce, stock
markets, wind power, and energy demand forecasting. An accurate forecast in these applications is an
essential and challenging task because of the complexity and uncertainty of time series. Nowadays,
deep learning methods are popular in time series forecasting and show better performance than
classical methods. However, in the literature, only some studies use deep learning methods in fuzzy
time series (FTS) forecasting. In this study, we propose a novel FTS forecasting model based upon the
hybridization of Recurrent Neural Networks with FTS to deal with the complexity and uncertainty of
these series. The proposed model utilizes Gated Recurrent Unit (GRU) to make predictions using a
combination of membership values and past values from original time series data as model input and
produce real forecast value. Moreover, the proposed model can handle first-order fuzzy relations and
high-order ones. In experiments, we have compared our model results with 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 similarly to
other methods. The proposed model is validated using the Covid-19 active case dataset and BIST100
Index dataset and performs better than Long Short-term Memory (LSTM) networks.