  Modeling inter-dependencies between time-series is the key to achieve high
performance in anomaly detection for multivariate time-series data. The
de-facto solution to model the dependencies is to feed the data into a
recurrent neural network (RNN). However, the fully connected network structure
underneath the RNN (either GRU or LSTM) assumes a static and complete
dependency graph between time-series, which may not hold in many real-world
applications. To alleviate this assumption, we propose a dynamic bipartite
graph structure to encode the inter-dependencies between time-series. More
concretely, we model time series as one type of nodes, and the time series
segments (regarded as event) as another type of nodes, where the edge between
two types of nodes describe a temporal pattern occurred on a specific time
series at a certain time. Based on this design, relations between time series
can be explicitly modelled via dynamic connections to event nodes, and the
multivariate time-series anomaly detection problem can be formulated as a
self-supervised, edge stream prediction problem in dynamic graphs. We conducted
extensive experiments to demonstrate the effectiveness of the design.
