Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
A new version of the rule induction system LERS
Fundamenta Informaticae
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Readings in Fuzzy Sets for Intelligent Systems
Readings in Fuzzy Sets for Intelligent Systems
Extending ERD modeling notation to fuzzy management of GIS data files
Data & Knowledge Engineering
A fuzzy seasonal ARIMA model for forecasting
Fuzzy Sets and Systems - Information processing
Design and implementation of finite resolution crisp and fuzzy spatial objects
Data & Knowledge Engineering
An improved accuracy measure for rough sets
Journal of Computer and System Sciences
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Learning fuzzy rules with their implication operators
Data & Knowledge Engineering
Rough clustering of sequential data
Data & Knowledge Engineering
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Trend-weighted fuzzy time-series model for TAIEX forecasting
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Mining closed patterns in multi-sequence time-series databases
Data & Knowledge Engineering
Forecasting tourism demand based on improved fuzzy time series model
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
Extracting fuzzy relations in fuzzy time series model based on approximation concepts
Expert Systems with Applications: An International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Alternative rule induction methods based on incremental object using rough set theory
Applied Soft Computing
High-order fuzzy-neuro expert system for time series forecasting
Knowledge-Based Systems
Mining effective multi-segment sliding window for pathogen incidence rate prediction
Data & Knowledge Engineering
Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach
Expert Systems with Applications: An International Journal
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This study proposes a hybrid fuzzy time series model with two advanced methods, cumulative probability distribution approach (CPDA) and rough set rule induction, to forecast stock markets. To improve forecasting accuracy, three refining processes of fuzzy time series are provided in the proposed model: (1) using CPDA to discretize the observations in training datasets based on the characteristics of data distribution, (2) generating rules (fuzzy logical relationships) by rough set algorithm and (3) producing forecasting results based on rule support values from rough set algorithm. To verify the forecasting performance of the proposed model in detail, two empirical stock markets (TAIEX and NYSE) are used as evaluating databases; two other methodologies, proposed by Chen and Yu, are used as comparison models, and two different evaluation methods (moving windows) are used. The proposed model shows a greatly improved performance in stock market forecasting compared to other fuzzy time series models.