Anomaly prediction for electronic resources

A computer-implemented method, comprising:

• determining a normal distribution and an anomaly distribution from historical time-series data;
• determining, for the normal distribution and the anomaly distribution, values for a set of distribution parameters;
• building a time-series forecasting model based upon the values for the set of distribution parameters, the time-series forecasting model comprising a machine learning model trained using probabilities for different anomalous events;
• and
• generating, using the time-series forecasting model, a prediction of a future occurrence of one or more anomalies.

A system, comprising:

• a processor;
• and
• memory including instructions that, when executed by the processor, cause the system to:
• determine an event distribution and an anomaly distribution from historical time-series data;
• determine, for the event distribution and the anomaly distribution, values for a set of distribution parameters;
• obtain a time-series forecasting model based upon the values for the set of distribution parameters, the time-series forecasting model comprising a machine learning model trained using probabilities for different anomalous events;
• and
• generate, using the time-series forecasting model, a prediction of a future occurrence of one or more anomalies.