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This paper applies meta-learning to classical ARIMA forecasting, training a policy on past experiments to recommend promising configurations for new series rather than fitting each one from scratch. It reports gains over AutoARIMA on M1, M3, and Tourism and competitive results against zero-shot foundation models such as Chronos2, Moirai2, and TimesFM. For ML readers, the useful point is that transfer at the configuration level can keep interpretable statistical models competitive with large pretrained forecasters, though the benchmarks are forecasting-specific and the comparison is limited to the cited datasets.
MetaARIMA: Configuration-Level Transfer Learning for ARIMA Models
arXiv:2610.34171v1 Announce Type: cross Abstract: Time Series Foundation Models have demonstrated that pretraining on diverse datasets enables effective transfer to new forecasting tasks. Classical methods like ARIMA, however, remain configured and trained independently for each series, discarding knowledge from previous experiments. Consequently, selecting and training an ARIMA configuration remains challenging and often relies on heuristic approaches based on in-sample goodnessof-fit measures. We propose MetaARIMA, a pretrained configuration policy based on metalearning that brings transfer learning to classical statistical forecasting at the configuration level rather than the model level. The policy learns from historical experiments which ARIMA configurations achieve top-quantile performance, and recommends promising candidates for new series while preserving interpretability and local estimation. At inference, it selects a diverse set of candidates via maximal marginal relevance and efficiently identifies the final configuration using successive halving. Experiments in a transfer learning setting, training on M4 and evaluating on M1, M3, and Tourism, show that MetaARIMA outperforms AutoARIMA and other classical methods while being more dataefficient. In a comparison with recent zero-shot foundation models, MetaARIMA remains competitive with Chronos2, Moirai2, and TimesFM. The experiments are publicly available at https://github.com/vcerqueira/experiments-metaarima.
Abstract:Time Series Foundation Models have demonstrated that pretraining on diverse datasets enables effective transfer to new forecasting tasks. Classical methods like ARIMA, however, remain configured and trained independently for each series, discarding knowledge from previous experiments. Consequently, selecting and training an ARIMA configuration remains challenging and often relies on heuristic approaches based on in-sample goodnessof-fit measures. We propose MetaARIMA, a pretrained configuration policy based on metalearning that brings transfer learning to classical statistical forecasting at the configuration level rather than the model level. The policy learns from historical experiments which ARIMA configurations achieve top-quantile performance, and recommends promising candidates for new series while preserving interpretability and local estimation. At inference, it selects a diverse set of candidates via maximal marginal relevance and efficiently identifies the final configuration using successive halving. Experiments in a transfer learning setting, training on M4 and evaluating on M1, M3, and Tourism, show that MetaARIMA outperforms AutoARIMA and other classical methods while being more dataefficient. In a comparison with recent zero-shot foundation models, MetaARIMA remains competitive with Chronos2, Moirai2, and TimesFM. The experiments are publicly available at this https URL.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.34171 [stat.ML]
(or arXiv:2610.34171v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.34171 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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