Multi-variable time series forecasting model based on high-order hesitant probabilistic linguistic fuzzy logical relationship

Abstract

The stock market is uncertain, but its fluctuations have inherent laws. A suitable method to extract these rules from historical data is crucial for predicting future trends. However, since these rules are often disturbed by external noise, noise reduction while preserving critical internal information is necessary to improve the accuracy of fuzzy time series forecasting. In this paper, we propose a novel two-factor high-order fuzzy time series (FTS) forecasting model based on hesitant probabilistic fuzzy logical relationship (HPLR). To evaluate the performance of the model, we conduct empirical analysis using the closing price of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the main factor and the opening price as the secondary factor. The proposed model shows improved prediction performance and is intelligent and interpretable in model design. In addition, we forecasted the Hang Seng Index (HSI) to further illustrate the generalizability of the model.