Techniques for detecting anomalous data points in time series data

Abstract

Techniques for increasing the precision of machine learning models that are trained to detect anomalous data points in a time series. The techniques including methods and systems for training machine learning models offline, using the trained machine learning models to predict anomalies in an online runtime environment, and updating anomaly detection models in the runtime and offline environments. The machine learning models may include a multitask model for predicting one or more anomalous events present in input time series data, and for each identified anomalous event type, predicting a type of machine learning model that is best suited for predicting that anomalous event type. The models may further include model instances selected using the predicted one or more anomalous events present in the input time series data and the predicted anomalous event type and used to predict an anomaly event in the input time series data.