A Genetic Rough Set Approach to Fuzzy Time-Series Prediction

Abstract

Fuzzy Time series (FTS) has been widely applied to handle non-linear problems, such as enrollment estimation, weather prediction and stock index forecasting. FTS predicted values on the basis of an equal interval, which is determined the early stages of forecasting in the model. In this paper, we employed Genetic Algorithms (GA) to optimize the interval at… (More)

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Cite this paper

@article{Watada2016AGR, title={A Genetic Rough Set Approach to Fuzzy Time-Series Prediction}, author={Junzo Watada and Jing Zhao and Yoshiyuki Matsumoto}, journal={2016 Third International Conference on Computing Measurement Control and Sensor Network (CMCSN)}, year={2016}, pages={20-23} }