TRAINING OF FORECASTING MODELS FOR TIME SERIES ANOMALY DETECTION APPLICATIONS

Predicted observations within a first window are forecast by a predictor and a residual signal is calculated based on a difference between predicted observations and true observations. A gradient of the residual signal is determined indicative of a degree of divergence. When the gradient is outside a predetermined range, the predictor is retrained. Based on further predicted observations forecast by the predictor, a confidence envelope for a second window is generated and, using the confidence envelope, it is determined whether an outlier portion exists within the second window.