Improving the ANFIS Forecating Model for Time Series Based on the Fuzzy Cluster Analysis Algorithm
| Autor: | Dinh Toan Pham, Dan Nguyenthihong, Tai Vovan |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Fuzzy System Applications. 11:1-20 |
| ISSN: | 2156-1761 2156-177X |
| DOI: | 10.4018/ijfsa.313602 |
| Popis: | This paper proposes the forecasting model for the time series based on the improvement of the adaptive neuro-fuzzy inference system (ANFIS) method and the fuzzy cluster analysis (FCA) algorithm. In this model, (i) the authors firstly find the appropriate number of groups for the series. Then, (ii) this study determines the specific elements for each group based on the established fuzzy relationship. Finally, using the results of (i) and (ii) as the input variables, the authors improve the iterations of ANFIS method. Combining the above improvements, the efficient forecasting model for time series is proposed. The proposed model is illustrated step by step through a numerical example, and implemented rapidly by the established Matlab procedure. The experiment obtained from this model shows the outstanding advantages in comparison with the existing ones. This research can be applied well to forecast for many fields in reality. |
| Databáze: | OpenAIRE |
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