  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 https://github.com/OrigamiSL/FDNet.
