AALF: Almost Always Linear Forecasting

• URL: http://arxiv.org/abs/2409.10142v1
• Date: Mon, 16 Sep 2024 10:13:09 GMT
• Title: AALF: Almost Always Linear Forecasting
• Authors: Matthias Jakobs, Thomas Liebig,
• Abstract summary: 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. An empirical study on various real-world datasets shows that our selection methodology outperforms state-of-the-art online model selections methods in most cases.
• Score: 3.336367986372977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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