The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Samingun Handoyo, Ying-Ping Chen
|DOI : 10.14445/22315381/IJETT-V68I12P220|
MLA Style: Samingun Handoyo, Ying-Ping Chen. The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part International Journal of Engineering Trends and Technology 68.12(2020):118-122.
APA Style:Samingun Handoyo, Ying-Ping Chen. The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part. International Journal of Engineering Trends and Technology, 68(12), 118-122.
The paper aims to build and implement a predictive model called a fuzzy system. The fuzzy rule bases component is generated by using the input-output data pairs. Its consequence part is optimized by using ordinary least squares. The initial structure model is needed to create the input-output data pairs based on the multiple time series. The rule bases are generated by using table lookup schema in which each input-output pairs has a contribution as a candidate rule. The obtaining rule base is modified to be an efficient one by optimizing its consequence part. As a case study is used, the 2-time series assumed which they have a causality effect. The data are the soybean price of both domestic and abroad. The developed fuzzy system is used in the forecasting of the domestic soybean price. The fuzzy system`s performance is very satisfying, assessed according to the R-squared and mean squared error of criteria.
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fuzzy system, optimized rule bases, predictive model, times series forecasting.