Multivariate time seriesAnomaly detectionSynthetic data setMachine learningDeep learning
A Java class that provides constructors and methods to generate synthetic data sets of multi-variate time series with/without anomalies. The class Random is used to introduce the right percentage of aleatority to the generation of the signals. Temporal patterns have been modeled based on trigonometric functions, randomly selected feature by feature. To reproduce the anomalies, a little noise is added to the generated signals. The class has been designed to test machine learning algorithms developed for anomaly detection in multivariate time series data.