Evolution strategies based coefficient of TSK fuzzy forecasting engine

Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evoluti...

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Bibliographic Details
Published inInternational journal of advances in intelligent informatics Vol. 7; no. 1; pp. 89 - 100
Main Authors Sari, Nadia Roosmalita, Mahmudy, Wayan Firdaus, Wibawa, Aji Prasetya
Format Journal Article
LanguageEnglish
Published Universitas Ahmad Dahlan 01.03.2021
Subjects
evolution strategies
forecasting
inflation rate
mean square error
tsk fuzzy logic
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Summary:Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance.
ISSN:2442-6571
2442-6571
DOI:10.26555/ijain.v7i1.376