Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting
It addresses forecasting accuracy for time-series applications, but is incremental as it highlights an overlooked baseline.
The paper tackles long sequence time-series forecasting by proposing historical inertia as a baseline, showing it can achieve up to 82% relative improvement over state-of-the-art methods.
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. In this paper, we introduce a new baseline for LSTF, the historical inertia (HI), which refers to the most recent historical data-points in the input time series. We experimentally evaluate the power of historical inertia on four public real-word datasets. The results demonstrate that up to 82\% relative improvement over state-of-the-art works can be achieved even by adopting HI directly as output.