1. A method for detecting anomalies in time series data, wherein the time series data is multivariate, comprising the steps of: partitioning time series training data into partitions; determining a representation for each partition in each time window to form a model of the time series training data, wherein the model includes representations of distributions of the time series training data; comparing representations obtained from partitions of time series test data to the model to obtain anomaly scores, wherein the steps are performed in a processor.