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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2021-03-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/376 |
| _version_ | 1818327169104871424 |
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| author | Nadia Roosmalita Sari Wayan Firdaus Mahmudy Aji Prasetya Wibawa |
| author_facet | Nadia Roosmalita Sari Wayan Firdaus Mahmudy Aji Prasetya Wibawa |
| author_sort | Nadia Roosmalita Sari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T12:11:59Z |
| format | Article |
| id | doaj.art-b727f03c81f142b0baf2bdebb3bfecd5 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-12-13T12:11:59Z |
| publishDate | 2021-03-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-b727f03c81f142b0baf2bdebb3bfecd52022-12-21T23:46:49ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612021-03-01718910010.26555/ijain.v7i1.376165Evolution strategies based coefficient of TSK fuzzy forecasting engineNadia Roosmalita Sari0Wayan Firdaus Mahmudy1Aji Prasetya Wibawa2Institut Agama Islam Negeri (IAIN) TulungagungUniversitas BrawijayaUniversitas Negeri MalangForecasting 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.http://ijain.org/index.php/IJAIN/article/view/376evolution strategiestsk fuzzy logicinflation rateforecastingmean square error |
| spellingShingle | Nadia Roosmalita Sari Wayan Firdaus Mahmudy Aji Prasetya Wibawa Evolution strategies based coefficient of TSK fuzzy forecasting engine IJAIN (International Journal of Advances in Intelligent Informatics) evolution strategies tsk fuzzy logic inflation rate forecasting mean square error |
| title | Evolution strategies based coefficient of TSK fuzzy forecasting engine |
| title_full | Evolution strategies based coefficient of TSK fuzzy forecasting engine |
| title_fullStr | Evolution strategies based coefficient of TSK fuzzy forecasting engine |
| title_full_unstemmed | Evolution strategies based coefficient of TSK fuzzy forecasting engine |
| title_short | Evolution strategies based coefficient of TSK fuzzy forecasting engine |
| title_sort | evolution strategies based coefficient of tsk fuzzy forecasting engine |
| topic | evolution strategies tsk fuzzy logic inflation rate forecasting mean square error |
| url | http://ijain.org/index.php/IJAIN/article/view/376 |
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