Dynamic Time Scan Forecasting

2019/06/12 by Marcelo Azevedo Costa, Leandro Brioschi Mineti, Costa, Marcelo Azevedo +5
Computer Science · Decision Sciences · #Advanced Statistical Process Monitoring #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Electrical engineering #Forecasting Techniques and Applications #Signal Processing (eess.SP) #Time Series Analysis and Forecasting #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1906.05399

openalex publication_date 2019/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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