Dynamic Time Scan Forecasting

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.