Abstract: A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.

Abstract: A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.

Abstract: A data prediction method to apply to a time series. In some embodiments, the data may be decomposed into a superposition of two or more components, which each represent different facets of the data. In further embodiments presented herein, the data may be decomposed into components representing: slowly-varying oscillations; cyclical and known instantaneous (non-stationary) disturbances; and background stationary noise effects. Each component may then be subjected to its own prediction algorithm. The predicted values of each component may then be composed to obtain a final prediction of the original data.