Dynamic Time Scan Forecasting
Marcelo Azevedo Costa, Leandro Brioschi Mineti, Marcos Oliveira, Prates, Ramiro Ruiz Cardenas

TL;DR
The paper introduces a dynamic time scan forecasting method that identifies similar historical patterns using a dynamically estimated similarity function to improve wind speed forecasting accuracy.
Contribution
It proposes a novel dynamic similarity estimation approach for time series forecasting that outperforms traditional statistical and machine learning methods.
Findings
Outperformed statistical and machine learning approaches in wind speed forecasting
Uses a dynamic similarity function instead of fixed distance measures
Provides both point forecasts and forecasting intervals
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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Taxonomy
TopicsForecasting Techniques and Applications · Time Series Analysis and Forecasting · Advanced Statistical Process Monitoring
