  We consider a setting where multiple entities inter-act with each other over
time and the time-varying statuses of the entities are represented as multiple
correlated time series. For example, speed sensors are deployed in different
locations in a road network, where the speed of a specific location across time
is captured by the corresponding sensor as a time series, resulting in multiple
speed time series from different locations, which are often correlated. To
enable accurate forecasting on correlated time series, we proposes graph
attention recurrent neural networks.First, we build a graph among different
entities by taking into account spatial proximity and employ a multi-head
attention mechanism to derive adaptive weight matrices for the graph to capture
the correlations among vertices (e.g., speeds at different locations) at
different timestamps. Second, we employ recurrent neural networks to take into
account temporal dependency while taking into account the adaptive weight
matrices learned from the first step to consider the correlations among time
series.Experiments on a large real-world speed time series data set suggest
that the proposed method is effective and outperforms the state-of-the-art in
most settings. This manuscript provides a full version of a workshop paper [1].
