1. A method for detecting anomalies in time series data, comprising the steps of: partitioning the time series data into overlapping and sliding time windows; determine features for each window; clustering features of similar windows to obtain universal features, wherein the universal feature includes a stochastic component, wherein statistics of the stochastic component include a mean, a standard deviation, a mean of an absolute value of a first difference, a number of mean crossings, a percentage of positive values in the first difference, a percentage of zero values in the first difference, and an average length of a run of positive differences of the time series data in the window, and standard deviations of the statistics; comparing the universal features extracted from testing time series data with the universal features acquired from training time series data to determine a score, wherein the universal features characterize trajectory components of the time series data and stochastic components of the time series data; and detecting an anomaly if the anomaly score is above a threshold, wherein the steps are performed in a processor.