  Time-series forecasting models often encounter abrupt changes in a given
period of time which generally occur due to unexpected or unknown events.
Despite their scarce occurrences in the training set, abrupt changes incur loss
that significantly contributes to the total loss. Therefore, they act as noisy
training samples and prevent the model from learning generalizable patterns,
namely the normal states. Based on our findings, we propose a reweighting
framework that down-weights the losses incurred by abrupt changes and
up-weights those by normal states. For the reweighting framework, we first
define a measurement termed Local Discrepancy (LD) which measures the degree of
abruptness of a change in a given period of time. Since a training set is
mostly composed of normal states, we then consider how frequently the temporal
changes appear in the training set based on LD. Our reweighting framework is
applicable to existing time-series forecasting models regardless of the
architectures. Through extensive experiments on 12 time-series forecasting
models over eight datasets with various in-output sequence lengths, we
demonstrate that applying our reweighting framework reduces MSE by 10.1% on
average and by up to 18.6% in the state-of-the-art model.
