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Long sequence time-series forecasting (LSTF) has become increasingly popularfor its wide range of applications. Though superior models have been proposedto enhance the prediction effectiveness and efficiency, it is reckless toneglect or underestimate one of the most natural and basic temporal propertiesof time-series. In this paper, we introduce a new baseline for LSTF, thehistorical inertia (HI), which refers to the most recent historical data-pointsin the input time series. We experimentally evaluate the power of historicalinertia on four public real-word datasets. The results demonstrate that up to82\% relative improvement over state-of-the-art works can be achieved even byadopting HI directly as output.

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