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Stock index forecasting: A new fuzzy time series forecasting method
Author(s) -
Wu Hao,
Long Haiming,
Wang Yue,
Wang Yanqi
Publication year - 2021
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2734
Subject(s) - computer science , data mining , stock market index , multivariate statistics , cluster analysis , fuzzy logic , index (typography) , time series , support vector machine , series (stratigraphy) , econometrics , partition (number theory) , probabilistic forecasting , statistics , artificial intelligence , mathematics , machine learning , stock market , probabilistic logic , geography , paleontology , context (archaeology) , archaeology , combinatorics , world wide web , biology
This paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe of discourse, and the representative exemplars are generated as defuzzied values. The SVR model is employed to explore the unrecognized relationships and modify the forecasts. In addition, the error‐based evaluation criteria are applied to evaluate the methods. The performance of the method is evaluated using the Taiwan Capitalization Weighted Stock Index (TAIEX), Standard & Poor's 500 Index (S&P500), and Dow Jones Industrial Average (DJIA) dataset, and the experimental results demonstrate that the proposed method outperforms some classic models.