Dynamic Time Scan Forecasting: A Benchmark With M4 Competition Data

Abstract

Univariate forecasting methods are fundamental for many different application areas. M-competitions provide important benchmarks for scientists, researchers, statisticians, and engineers in the field, for evaluating and guiding the development of new forecasting techniques. In this paper, the Dynamic Time Scan Forecasting (DTSF), a new univariate forecasting method based on scan statistics, is presented. DTSF scans an entire time series, identifies past patterns whichare similar to the last available observations and forecasts based on the median of the subsequent observations of the most similar windows in past. In order to evaluate the performance of this method, a comparison with other statistical forecasting methods, applied in the M4 competition, is provided. In the hourly domain, an average sMAPE of 12.9% was achieved using hte method with the default parameters, while the baseline competition - the simple average of the forecasts of Holt, Damped and Theta methods - was 22.1%. The method proved to be competitive in longer time series, with high repeatability.