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

Junhong Gao, Yuyan Wang, T. C.Edwin Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
JournalJournal of Control and Decision
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • forecasting
  • fuzzy logical relationship
  • Fuzzy time series
  • hesitant probabilistic fuzzy set

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Control and Optimization
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Multi-variable time series forecasting model based on high-order hesitant probabilistic linguistic fuzzy logical relationship'. Together they form a unique fingerprint.

Cite this