AALF: Almost Always Linear Forecasting
<|reference_start|>AALF: Almost Always Linear Forecasting: Recent works for time-series forecasting more and more leverage the high predictive power of Deep Learning models. With this increase in model complexity, however, comes a lack in understanding of the underlying model decision process, which is problematic for high-stakes decision making. At the same time, simple, interpretable forecasting methods such as Linear Models can still perform very well, sometimes on-par, with Deep Learning approaches. We argue that simple models are good enough most of the time, and forecasting performance can be improved by choosing a Deep Learning method only for certain predictions, increasing the overall interpretability of the forecasting process. In this context, we propose a novel online model selection framework which uses meta-learning to identify these predictions and only rarely uses a non-interpretable, large model. An extensive empirical study on various real-world datasets shows that our selection methodology outperforms state-of-the-art online model selections methods in most cases. We find that almost always choosing a simple Linear Model for forecasting results in competitive performance, suggesting that the need for opaque black-box models in time-series forecasting is smaller than recent works would suggest.<|reference_end|>
@article{jakobs2024aalf:,
    title={AALF: Almost Always Linear Forecasting},
    author={Matthias Jakobs, Thomas Liebig},
    journal={arXiv preprint arXiv:2409.10142},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10142},
    primaryClass={cs.LG}
}