  A reliable and efficient representation of multivariate time series is
crucial in various downstream machine learning tasks. In multivariate time
series forecasting, each variable depends on its historical values and there
are inter-dependencies among variables as well. Models have to be designed to
capture both intra- and inter-relationships among the time series. To move
towards this goal, we propose the Time Series Attention Transformer (TSAT) for
multivariate time series representation learning. Using TSAT, we represent both
temporal information and inter-dependencies of multivariate time series in
terms of edge-enhanced dynamic graphs. The intra-series correlations are
represented by nodes in a dynamic graph; a self-attention mechanism is modified
to capture the inter-series correlations by using the super-empirical mode
decomposition (SMD) module. We applied the embedded dynamic graphs to times
series forecasting problems, including two real-world datasets and two
benchmark datasets. Extensive experiments show that TSAT clearly outerperforms
six state-of-the-art baseline methods in various forecasting horizons. We
further visualize the embedded dynamic graphs to illustrate the graph
representation power of TSAT. We share our code at
https://github.com/RadiantResearch/TSAT.
