Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory

DOI10.1016/j.matcom.2019.01.001OpenAlexW2912784131WikidataQ128549888 ScholiaQ128549888MaRDI QIDQ1997570

Samarjit Kar, Shanoli Samui Pal

Publication date: 2 March 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2019.01.001

zbMATH Keywords

time series forecastingfuzzisticsfirst order fuzzy logicrough set based rule reductionstock market data

Mathematics Subject Classification ID

Inference from stochastic processes (62Mxx)

Cites Work

• Forecasting the stock market with linguistic rules generated from the minimize entropy principle and the cumulative probability distribution approaches
• Global discretization of continuous attributes as preprocessing for machine learning
• Generalized autoregressive conditional heteroscedasticity
• INTUITIONISTIC FUZZY SETS BASED METHOD FOR FUZZY TIME SERIES FORECASTING
• PROBABILISTIC AND INTUITIONISTIC FUZZY SETS–BASED METHOD FOR FUZZY TIME SERIES FORECASTING
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Rough sets
• Time series forecasting using fuzzy transformation and neural network with back propagation learning
• Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance
• FUSINTER: A Method for Discretization of Continuous Attributes
• Fuzzy sets

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