# Automated univariate time series forecasting with regression trees

**Authors:** Francisco Martínez, María P. Frías  
**arXiv:** [2602.00077](https://arxiv.org/abs/2602.00077)  
**Date:** 2026-01-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2602.00077v1/

## Key points

- Methodology for automated univariate time series forecasting using regression trees and their ensembles (bagging and random forests)
- Key aspects addressed: autoregressive approach, recursive forecasts, selecting autoregressive features, handling trending series, managing seasonal behavior
- Experimental results show forecast accuracy comparable to well-established statistical models like exponential smoothing or ARIMA
- Development of publicly available software implementing proposed strategies discussed
- Importance of time series forecasting in domains such as sales, health, energy, and human resources
- Value of automatic tools in fields like retail sales with numerous series needing short-term forecasts
- Univariate approach eliminates need for external variables, simplifying process by avoiding parameter tuning
- Regression trees offer automatic feature selection capabilities for forecasting
- Bagging and random forests perform well without requiring extensive parameter tuning due to default values
- Gradient boosting machines not considered due to requirement for precise tuning
- Study highlights effectiveness of regression trees and ensembles in achieving accurate forecasts without extensive parameter tuning or feature selection efforts

## Summary

This paper presents a methodology for automated univariate time series forecasting using regression trees and their ensembles, specifically bagging and random forests. The focus is on addressing key aspects such as utilizing an autoregressive approach, recursive forecasts, selecting autoregressive features, handling trending series, and managing seasonal behavior. Experimental results demonstrate that the forecast accuracy achieved is comparable to well-established statistical models like exponential smoothing or ARIMA. Additionally, the development of publicly available software implementing all proposed strategies is discussed. Time series forecasting plays a vital role in various domains including sales, health, energy, and human resources. Automatic tools are particularly valuable in fields like retail sales where numerous series need to be forecast within short timeframes. By adopting an univariate time series forecasting approach that solely relies on the values within the series for predictions, the need for external variables is eliminated. This simplifies the forecasting process by avoiding parameter tuning which can be computationally intensive and may yield suboptimal results with short training sets. Regression trees offer promising properties for automated time series forecasting due to their automatic feature selection capabilities. Bagging and random forests are especially advantageous as they typically perform well without requiring parameter tuning thanks to their default values. The paper outlines a methodology that leverages regression trees along with bagging and random forests for automated univariate time series forecasting. While gradient boosting machines present another option for combining regression trees, they were not considered due to their requirement for precise tuning. In conclusion, this study provides a detailed exploration of automated univariate time series forecasting using regression trees and their ensembles. The findings highlight the effectiveness of this approach in achieving accurate forecasts without the need for extensive parameter tuning or feature selection efforts. The availability of publicly accessible software implementing these strategies further enhances the practical applicability of the proposed methodology in real-world forecasting scenarios.