ML Anthology

MASTFM: Meta-Learning and Data Augmentation to Stress Test Forecasting Models

Ricardo Inácio, Vítor Cerqueira, Marília Barandas, Carlos Soares
ECML-PKDD 2025 pp. 427-431
doi:10.1007/978-3-032-06129-4_25 /ecmlpkdd/2025/inacio2025ecmlpkdd-mastfm/

Abstract

Time series forecasting is pivotal across industries, as it fosters data-driven decision-making, increasing the chances of successful outcomes. Yet, certain instances that feature adverse characteristics, may lead models to manifest stress through decreases in performance (e.g., large errors). Hence, the ability to preemptively identify such cases, while establishing their root causes, would be advantageous to elevate the understanding of forecasting processes, informing users about the trustworthiness of predictions. Hence, we propose MASTFM , a method based on meta-learning that leverages statistical characteristics of input time series, and estimations of forecasting performance from model outputs, to build a metamodel that learns conditions for stress. Given that such occurrences are naturally rare, data augmentation is employed to ensure balance during training. Moreover, SHapley Additive exPlanations (SHAP) are used to explain how features impact forecasting behaviour.

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Cite

Text

Inácio et al. "MASTFM: Meta-Learning and Data Augmentation to Stress Test Forecasting Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_25

Markdown

[Inácio et al. "MASTFM: Meta-Learning and Data Augmentation to Stress Test Forecasting Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/inacio2025ecmlpkdd-mastfm/) doi:10.1007/978-3-032-06129-4_25

BibTeX

@inproceedings{inacio2025ecmlpkdd-mastfm,
  title     = {{MASTFM: Meta-Learning and Data Augmentation to Stress Test Forecasting Models}},
  author    = {Inácio, Ricardo and Cerqueira, Vítor and Barandas, Marília and Soares, Carlos},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2025},
  pages     = {427-431},
  doi       = {10.1007/978-3-032-06129-4_25},
  url       = {https://mlanthology.org/ecmlpkdd/2025/inacio2025ecmlpkdd-mastfm/}
}