  Feature engineering is required to obtain better results for time series
forecasting, and decomposition is a crucial one. One decomposition approach
often cannot be used for numerous forecasting tasks since the standard time
series decomposition lacks flexibility and robustness. Traditional feature
selection relies heavily on preexisting domain knowledge, has no generic
methodology, and requires a lot of labor. However, most time series prediction
models based on deep learning typically suffer from interpretability issue, so
the "black box" results lead to a lack of confidence. To deal with the above
issues forms the motivation of the thesis. In the paper we propose TSDFNet as a
neural network with self-decomposition mechanism and an attentive feature
fusion mechanism, It abandons feature engineering as a preprocessing convention
and creatively integrates it as an internal module with the deep model. The
self-decomposition mechanism empowers TSDFNet with extensible and adaptive
decomposition capabilities for any time series, users can choose their own
basis functions to decompose the sequence into temporal and generalized spatial
dimensions. Attentive feature fusion mechanism has the ability to capture the
importance of external variables and the causality with target variables. It
can automatically suppress the unimportant features while enhancing the
effective ones, so that users do not have to struggle with feature selection.
Moreover, TSDFNet is easy to look into the "black box" of the deep neural
network by feature visualization and analyze the prediction results. We
demonstrate performance improvements over existing widely accepted models on
more than a dozen datasets, and three experiments showcase the interpretability
of TSDFNet.
