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• W2951446178 abstract "The dynamic time scan forecasting method relies on the premise that the most important pattern in a time series precedes the forecasting window, i.e., the last observed values. Thus, a scan procedure is applied to identify similar patterns, or best matches, throughout the time series. As oppose to euclidean distance, or any distance function, a similarity function is dynamically estimated in order to match previous values to the last observed values. Goodness-of-fit statistics are used to find the best matches. Using the respective similarity functions, the observed values proceeding the best matches are used to create a forecasting pattern, as well as forecasting intervals. Remarkably, the proposed method outperformed statistical and machine learning approaches in a real case wind speed forecasting problem." @default.
• W2951446178 created "2019-06-27" @default.
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• W2951446178 date "2019-06-12" @default.
• W2951446178 modified "2023-09-23" @default.
• W2951446178 title "Dynamic Time Scan Forecasting" @default.
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