This paper presents FDNet: a Focal Decomposed Network for efficient, robust
and practical time series forecasting. We break away from conventional deep
time series forecasting formulas which obtain prediction results from universal
feature maps of input sequences. In contrary, FDNet neglects universal
correlations of input elements and only extracts fine-grained local features
from input sequence. We show that: (1) Deep time series forecasting with only
fine-grained local feature maps of input sequence is feasible upon theoretical
basis. (2) By abandoning global coarse-grained feature maps, FDNet overcomes
distribution shift problem caused by changing dynamics of time series which is
common in real-world applications. (3) FDNet is not dependent on any inductive
bias of time series except basic auto-regression, making it general and
practical. Moreover, we propose focal input sequence decomposition method which
decomposes input sequence in a focal manner for efficient and robust
forecasting when facing Long Sequence Time series Input (LSTI) problem. FDNet
achieves competitive forecasting performances on six real-world benchmarks and
reduces prediction MSE by 38.4% on average compared with other thirteen SOTA
baselines. The source code is available at this https URL

本研究提出了 FDNet，一个面向高效、鲁棒、实际的时序预测模型，利用局部特征图从输入序列中提取精细的局部特征，实现了在没有全局粗略特征图的情况下的深度时序预测，并通过焦点输入序列分解方法解决了长序列输入问题。在六项现实世界基准测试中，FDNet 取得了竞争性的预测结果，并平均减少了 38.4％的预测 MSE 与其他 13 个基线相比。