| Autor: |
Aji, Bagus Kohar, Wulandari, Ratri, Irawanto, Bambang, Surarso, Bayu, Farikhin, Farikhin, Dasril, Yosza Bin |
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2867 Issue 1, p1-5, 5p |
| Abstrakt: |
Fuzzy time series has gained widespread popularity as a forecasting method in recent years, with many researchers striving to enhance its performance by modifying clustering steps. This discussion focuses on the clustering process, specifically dividing it into two methods: the Kumar Method and the Cross Association Method. In the experiment, forecasts were computed using both methods, and the results, along with error values, were compared. The findings reveal that the Kumar Method demonstrates superior forecasting accuracy. This is substantiated by the smaller values of Mean Squared Error (MSE) and Average Forecasting Errors (AFER) at 197607.2 and 1.212%, respectively. In contrast, the Cross Association Method yielded higher MSE and AFER values at 304255.9163 and 1.128%, indicating a comparatively lower accuracy in forecasting. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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