Comparing Accuracy of Time Series Forecasting Methods

Abstract

It is difficult to decide which model or method should be chosen to accomplish the task of time series forecasting. The purpose of this research is to create a simple experimental framework for selecting time series forecasting methods by employing an optimal balance of statistical and machine learning models as representative methods. We adopted benchmarks from the M4 Competition and added gradient boosting and other methods commonly used in machine learning competitions. Accordingly, experiments were conducted to compare the accuracy of time series forecasting methods using data from the M4 Competition.