I am applying H2o autoencoder for anomaly detection for multivariate time series data. I have many time series data for different metrics of network elements which are recorded every 15 minutes. I want to find out that:At which timestamp that the anomaly happensWhat is/are the main metric(s) causing the anomalyI form the data matrix with columns as metrics and rows as the values of the metrics at a specific timestamp. For example, [[9:15, v1, v2, v3...], [9:30, v4, v5, v6, ...], ...]. I then train the data with autoencoder and use h2o.anomaly to check which data points have reconstruction MSE greater than the 99% percentile as the suspected anomalies. And these give me the timestamp when the possible anomalies happen.By using this method, can I get the information of the main metrics causing the anomaly?Thanks in advance.
H2o autoencoder anomaly detection for multivariate time series data
time series;anomaly detection;autoencoder