  Time series forecasting is a significant problem in many applications, e.g.,
financial predictions and business optimization. Modern datasets can have
multiple correlated time series, which are often generated with global (shared)
regularities and local (specific) dynamics. In this paper, we seek to tackle
such forecasting problems with DeepDGL, a deep forecasting model that
disentangles dynamics into global and local temporal patterns. DeepDGL employs
an encoder-decoder architecture, consisting of two encoders to learn global and
local temporal patterns, respectively, and a decoder to make multi-step
forecasting. Specifically, to model complicated global patterns, the vector
quantization (VQ) module is introduced, allowing the global feature encoder to
learn a shared codebook among all time series. To model diversified and
heterogenous local patterns, an adaptive parameter generation module enhanced
by the contrastive multi-horizon coding (CMC) is proposed to generate the
parameters of the local feature encoder for each individual time series, which
maximizes the mutual information between the series-specific context variable
and the long/short-term representations of the corresponding time series. Our
experiments on several real-world datasets show that DeepDGL outperforms
existing state-of-the-art models.
