Review for NeurIPS paper: Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Summary and Contributions: This paper addresses the problem of multivariate time-series prediction. The premise of the problem is, given N possibly correlated time series, predict the next H time steps for each of the time series. The paper develops over existing methods by proposing a novel deep neural network based algorithm that simultaneously accounts for the "spatial" and temporal correlations. The proposed algorithm first constructs an adjacency matrix to capture the similarity between the different time series by using a self-attention based similarity measure. Post this, the data is passed through two "stemGNN" blocks, with each block as described below.