Dynamic Time Scan Forecasting
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.
Cite
@article{arxiv.1906.05399,
title = {Dynamic Time Scan Forecasting},
author = {Marcelo Azevedo Costa and Leandro Brioschi Mineti and Marcos Oliveira Prates and Ramiro Ruiz Cardenas},
journal= {arXiv preprint arXiv:1906.05399},
year = {2019}
}
Comments
15 pages, 7 figures, working paper, version 1