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...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 inter-nal information is necessary to improve the accuracy of fuzzy time series forecasting.In thispaper,we propose a novel two-factor high-order fuzzy time series(FTS)forecasting model based on hesitant probabillistic 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 intelli-gent and interpretable in model design.In addition,we forecasted the Hang Seng Index(HSI)to further illustrate the generalizability of the model.展开更多
基金supported by Self Cultivation Innovation Team Project of Jinan:[Grant Number 202228075]Taishan Scholar Foundation of Shandong Province:[Grant Number tsqn202211197l+2 种基金the National Natural Science Foundation of China:[Grant Number 7237114471971129]Youth Innovation Technology Project of Higher School in Shandong Province:[Grant Number 2019RWG017].
文摘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 inter-nal information is necessary to improve the accuracy of fuzzy time series forecasting.In thispaper,we propose a novel two-factor high-order fuzzy time series(FTS)forecasting model based on hesitant probabillistic 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 intelli-gent and interpretable in model design.In addition,we forecasted the Hang Seng Index(HSI)to further illustrate the generalizability of the model.
