Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting

Abstract

Long sequence time-series forecasting (LSTF) has become increasingly popular for its wide range of applications. Though superior models have been proposed to enhance the prediction effectiveness and efficiency, it is reckless to neglect or underestimate one of the most natural and basic temporal properties of time series: history has inertia. In this paper, we introduce a new baseline for LSTF, named historical inertia (HI). In HI, the most recent historical data points in the input time series are adopted as the prediction results. We experimentally evaluate HI on 4 public real-world datasets and 2 LSTF tasks. The results demonstrate that up to 82% relative improvement over state-of-the-art works can be achieved. We further discuss why HI works and potential ways of benefiting from it.