diff --git a/README.md b/README.md index f9b018d8..98401c5b 100644 --- a/README.md +++ b/README.md @@ -139,9 +139,19 @@ Example from SMAP test set: Example from MSL test set (note that one anomaly segment is not detected): drawing +## Model Overview + +drawing + +Figure above adapted from [Zhao et al. (2020)](https://arxiv.org/pdf/2009.02040.pdf) + +1. The raw input data is preprocessed, and then a 1-D convolution is applied in the temporal dimension in order to smooth the data and alleviate possible noise effects. +2. The output of the 1-D convolution module is processed by two parallel graph attention layer, one feature-oriented and one time-oriented, in order to capture dependencies among features and timestamps, respectively. +3. The output from the 1-D convolution module and the two GAT modules are concatenated and fed to a GRU layer, to capture longer sequential patterns. +4. The output from the GRU layer are fed into a forecasting model and a reconstruction model, to get a prediction for the next timestamp, as well as a reconstruction of the input sequence. ## GAT layers - +Below we visualize how the two GAT layers view the input as a complete graph. Feature-Oriented GAT layer | Time-Oriented GAT layer --- | --- drawing | drawing