AALF: Almost Always Linear Forecasting

TL;DR

This paper introduces AALF, a simple yet effective online model selection framework that predominantly uses linear models for time-series forecasting, achieving competitive results with greater interpretability.

Contribution

The paper proposes a novel online model selection method that favors simple linear models, demonstrating that they often match complex models' performance with enhanced interpretability.

Findings

Linear models perform competitively with deep learning methods.

The proposed selection framework is more interpretable than existing methods.

Simple models suffice for most forecasting tasks.

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 application scenarios. At the same time, simple, interpretable forecasting methods such as ARIMA still perform very well, sometimes on-par, with Deep Learning approaches. We argue that simple models are good enough most of the time, and that forecasting performance could be improved by choosing a Deep Learning method only for few, important predictions, increasing the overall interpretability of the forecasting process. In this context, we propose a novel online model selection framework which learns to identify these predictions. An extensive empirical study on various real-world datasets shows that our selection methodology performs comparable to state-of-the-art online model selections methods in most cases while being significantly more interpretable. We find that almost always choosing a simple autoregressive linear model for forecasting results in competitive performance, suggesting that the need for opaque black-box models in time-series forecasting might be smaller than recent works would suggest.