Anomaly detection data workflow for time series data

A method comprising:

• determining a temporal trait associated with a timeseries dataset of a plurality of timeseries datasets, the temporal trait identifying a pattern within the timeseries dataset;
• selecting, from a first plurality of anomaly detection models and based on determining the temporal trait, a first anomaly detection model trained to detect anomalies in datasets classified under the temporal trait;
• determining a model execution parameter of a plurality of model execution parameters for the first anomaly detection model based on a timeseries signal associated with the timeseries dataset;
• receiving, based on selecting the first anomaly detection model and determining the model execution parameter, a plurality of sets of timestamps by inputting the timeseries dataset into the first anomaly detection model, wherein each set of the plurality of sets of timestamps includes a corresponding plurality of timestamps, each timestamp representing an anomaly detected within the timeseries dataset;
• generating, based on the plurality of sets of the timestamps, an anomaly timeseries dataset having a corresponding aggregated value representing timestamps for a plurality of time intervals;
• inputting the anomaly timeseries dataset into a second anomaly detection model to obtain one or more anomalies, wherein the anomaly timeseries dataset stores aggregated values generated from the plurality of sets of timestamps that represent anomalies within the plurality of sets of timestamps, and wherein the second anomaly detection model is not one of the first plurality of anomaly detection models;
• and
• generating a message based on the one or more anomalies.

One or more non-transitory, computer-readable media for correlating events based on anomalies occurring within a given time interval across multiple timeseries datasets, storing instructions that, when executed by one or more processors, cause operations comprising:

• determining a temporal trait associated with a timeseries dataset of a plurality of timeseries datasets, the temporal trait identifying a pattern within the timeseries dataset;
• selecting, from a first plurality of anomaly detection models and based on determining the temporal trait, a first anomaly detection model trained to detect anomalies in datasets classified under the temporal trait;
• determining a model execution parameter of a plurality of model execution parameters for the first anomaly detection model based on a timeseries signal associated with the timeseries dataset;
• receiving, based on selecting the first anomaly detection model and determining the model execution parameter, a plurality of sets of timestamps by inputting the timeseries dataset into the first anomaly detection model, wherein each set of the plurality of sets of timestamps includes a plurality of timestamps, each timestamp representing an anomaly detected within the timeseries dataset;
• generating, based on the plurality of sets of the timestamps, an anomaly timeseries dataset having a corresponding aggregated value representing timestamps for a plurality of time intervals;
• inputting the anomaly timeseries dataset into a second anomaly detection model to obtain one or more anomalies, wherein the anomaly timeseries dataset stores aggregated values generated from the plurality of sets of timestamps that represent anomalies within the plurality of sets of timestamps, and wherein the second anomaly detection model is not one of the first plurality of anomaly detection models;
• and
• generating a message based on the one or more anomalies.