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- 저자 : Mahdi Mazinani (검색결과 1 건)
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Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction
Aref Safari,Rahil Hosseini,Mahdi Mazinani 한국지능시스템학회 2021 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.21 No.4
Prediction of time series is associated with nondeterministic pattern analysis for uncertain conditions. Therefore, it is necessary to develop high-quality prediction methods for real-world applications. Type-2 fuzzy systems can handle high-order uncertainties, such as sequential dependencies associated with time series. Precise and reliable prediction can help to develop reasonable strategies and assist specialists in planning the best policies for modeling events in uncertain time series. In this study, a hybrid model (dynamic type-2 fuzzy time warping [DT2FTW]) was proposed for handling high-order uncertainties in time-series prediction. A type-2 fuzzy intelligent system was developed alongside a dynamic time warping algorithm for predicting the patterns’ similarity in long-time series for time-series prediction. The results demonstrate that the proposed DT2FTW model yields more reliable predictions on global standard benchmarks such as the Mackey-Glass, Dow Jones, and NASDAQ time-series. The results also confirm that the proposed DT2FTW model has lower error rates than its counterpart algorithms in terms of the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). In addition, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 94%, with the 95% confidence interval (92%-95%).
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