1. A method for detecting an anomaly in multivariate time series, comprising the steps of: determining a similarity matrix of nonnegative pairwise similarities between pairs of normal univariate time series data each of which corresponds to a variable representing a dimension of the multivariate time series data; applying a spectral clustering procedure to the similarity matrix in order to transform the similarity matrix into a block diagonal form, by partitioning the variables representing dimensions of the multivariate time series data into groups, wherein the groups are mutually exclusive; estimating a probability density model of normal behavior for each group and defining a factored probability distribution model over an entire multivariate time series as a product of the probability densities over each group; determining, for the multivariate time series data, an anomaly score using the probability density model of normal behavior for each group; and comparing the anomaly score to a predetermined threshold to signal the anomaly, wherein the steps are performed in a processor.