  Transformers have shown great power in time series forecasting due to their
global-range modeling ability. However, their performance can degenerate
terribly on non-stationary real-world data in which the joint distribution
changes over time. Previous studies primarily adopt stationarization to
attenuate the non-stationarity of original series for better predictability.
But the stationarized series deprived of inherent non-stationarity can be less
instructive for real-world bursty events forecasting. This problem, termed
over-stationarization in this paper, leads Transformers to generate
indistinguishable temporal attentions for different series and impedes the
predictive capability of deep models. To tackle the dilemma between series
predictability and model capability, we propose Non-stationary Transformers as
a generic framework with two interdependent modules: Series Stationarization
and De-stationary Attention. Concretely, Series Stationarization unifies the
statistics of each input and converts the output with restored statistics for
better predictability. To address the over-stationarization problem,
De-stationary Attention is devised to recover the intrinsic non-stationary
information into temporal dependencies by approximating distinguishable
attentions learned from raw series. Our Non-stationary Transformers framework
consistently boosts mainstream Transformers by a large margin, which reduces
MSE by 49.43% on Transformer, 47.34% on Informer, and 46.89% on Reformer,
making them the state-of-the-art in time series forecasting. Code is available
at this repository: https://github.com/thuml/Nonstationary_Transformers.
