Anomaly prediction for electronic resources

Abstract

Historical time-series data can be analyzed using a probabilistic model to determine one or more distributions, including at least a normal distribution and an anomaly distribution. These distributions can be analyzed to obtain values for distribution parameters, such as mean, standard deviation, and density, as well as other statistical parameters, for use in building a forecasting model. This model can analyze the time-series data to predict or forecast actionable anomalies at one or more future points or periods in time, such as may exceed a determined anomaly threshold with at least a minimum amount of confidence. A determination can be made as to one or more actions to take in anticipation of the anomalous event, or volume of events, such as to attempt to prevent the occurrence or to be better positioned to handle the occurrence. Such forecasting or prediction can utilize both modeling and feature engineering.