# Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms

arXiv: https://arxiv.org/abs/1908.08489 · HTML: https://mlsanity.com/paper/1908.08489/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves](https://mlsanity.com/paper/2108.08723/) (2021)
- [Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion](https://mlsanity.com/paper/2203.03279/) (2022)
- [Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting](https://mlsanity.com/paper/2303.11000/) (2023)
- [Forecast-Then-Optimize Deep Learning Methods](https://mlsanity.com/paper/2506.13036/) (2025)

## Abstract (arXiv)

One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inputs for meta-learners; evaluating the effect of forecasting error measures; and assessing the role of the dimensionality of the feature space on the forecasting errors of meta-learners. All of these objectives are achieved with the help of a diverse state-of-the-art pool of forecasters and meta-learners. For this purpose, first, a pool of forecasting algorithms is implemented on the NN5 competition dataset and ranked based on the two error measures. Then, six machine-learning classifiers known as meta-learners, are trained on the extracted features of the time series in order to assign the most suitable forecasting method for the various subsets of the pool of forecasters. Furthermore, two-dimensionality reduction methods are implemented in order to investigate the role of feature space dimension on the performance of meta-learners. In general, it was found that meta-learners were able to defeat all of the individual benchmark forecasters; this performance was improved even after applying the feature selection method.