Transformers have demonstrated impressive strength in long-term series
forecasting.
Existing prediction research mostly focused on mapping past short sub-series
(lookback window) to future series (forecast window). The longer training
dataset time series will be discarded, once training is completed. Models can
merely rely on lookback window information for inference, which impedes models
from analyzing time series from a global perspective. And these windows used by
Transformers are quite narrow because they must model each time-step therein.
Under this point-wise processing style, broadening windows will rapidly exhaust
their model capacity. This, for fine-grained time series, leads to a bottleneck
in information input and prediction output, which is mortal to long-term series
forecasting. To overcome the barrier, we propose a brand-new methodology to
utilize Transformer for time series forecasting. Specifically, we split time
series into patches by day and reform point-wise to patch-wise processing,
which considerably enhances the information input and output of Transformers.
To further help models leverage the whole training set's global information
during inference, we distill the information, store it in time representations,
and replace series with time representations as the main modeling entities. Our
designed time-modeling Transformer -- Dateformer yields state-of-the-art
accuracy on 7 real-world datasets with a 33.6\% relative improvement and
extends the maximum forecast range to half-year.

通过将时间序列分成按天分割的小块，重新进行逐块方式处理并设计时间建模 Transformer-Dateformer，以提高 Transformers 模型的信息输入和输出并使模型能够全局分析时间序列，大幅提高了时间序列预测的准确性和半年的最大预测范围。