Automated univariate time series forecasting with regression trees

TL;DR

This paper presents an automated approach for univariate time series forecasting using regression trees and ensembles, addressing feature selection, trend, and seasonality, with results comparable to traditional statistical models.

Contribution

It introduces a novel methodology combining regression trees with autoregressive features for automated univariate time series forecasting.

Findings

Forecast accuracy comparable to exponential smoothing and ARIMA

Developed a publicly available software implementation

Effective handling of trends and seasonal patterns

Abstract

This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregressive approach and recursive forecasts, how to select the autoregressive features, how to deal with trending series and how to cope with seasonal behavior. Experimental results show a forecast accuracy comparable with well-established statistical models such as exponential smoothing or ARIMA. Furthermore, a publicly available software implementing all the proposed strategies has been developed and is described in the paper.