  Anomaly detection of time series, especially multivariate time series(time
series with multiple sensors), has been focused on for several years. Though
existing method has achieved great progress, there are several challenging
problems to be solved. Firstly, existing method including neural network only
concentrate on the relationship in terms of timestamp. To be exact, they only
want to know how does the data in the past influence which in the future.
However, one sensor sometimes intervenes in other sensor such as the speed of
wind may cause decrease of temperature. Secondly, there exist two categories of
model for time series anomaly detection: prediction model and reconstruction
model. Prediction model is adept at learning timely representation while short
of capability when faced with sparse anomaly. Conversely, reconstruction model
is opposite. Therefore, how can we efficiently get the relationship both in
terms of both timestamp and sensors becomes our main topic. Our approach uses
GAT, which is originated from graph neural network, to obtain connection
between sensors. And LSTM is used to obtain relationships timely. Our approach
is also designed to be double headed to calculate both prediction loss and
reconstruction loss via VAE(Variational Auto-Encoder). In order to take
advantage of two sorts of model, multi-task optimization algorithm is used in
this model.
