  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.
