Generating synthetic time series datasets having anomalies

A method comprising:

• receiving a user input comprising a command to generate a synthetic time series dataset;
• determining, based on the user input, a percentage of anomalies and a time period for anomalies to be generated for the synthetic time series dataset;
• determining, based on the percentage and the time period, a number of anomalies to generate within the synthetic time series dataset;
• generating, for a plurality of time slots of the synthetic time series dataset, (1) a first plurality of data points using a first function from a first plurality of available functions, (2) a second plurality of data points using a second function from a second plurality of available functions, and (3) a third plurality of data points using a third function from a third plurality of functions;
• determining a minimum anomaly variance and a maximum anomaly variance for generating one or more anomalies for the synthetic time series dataset, wherein the minimum anomaly variance defines a minimum change of an anomaly relative to a point variance of the third plurality of data points and the maximum anomaly variance defines a maximum change of the anomaly relative to the point variance of the third plurality of data points;
• generating the one or more anomalies based on applying, to one or more data points in the third plurality of data points, a corresponding anomaly variance generated based on the maximum anomaly variance and the minimum anomaly variance;
• and
• storing, according to the plurality of time slots, the synthetic time series dataset comprising the one or more anomalies by combining the first plurality of data points, the second plurality of data points, and the third plurality of data points into corresponding time slots of the plurality of time slots.

One or more non-transitory, computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:

• receiving a user input comprising a command to generate a synthetic time series dataset;
• generating, for a plurality of time slots of the synthetic time series dataset, (1) a first plurality of data points using a first function from a first plurality of available functions, (2) a second plurality of data points using a second function from a second plurality of available functions, and (3) a third plurality of data points using a third function of a third plurality of functions;
• scaling the third plurality of data points such that a relationship between the second plurality of data points and the third plurality of data points satisfies a ratio retrieved from the user input, wherein the ratio balances the relationship;
• determining a minimum variance and a maximum variance, wherein the minimum variance defines a minimum change relative to a total point variance of the third plurality of data points and the maximum variance defines a maximum change relative to the total point variance of the third plurality of data points;
• applying, to one or more data points in the third plurality of data points, a corresponding variance generated based on the maximum variance and the minimum variance;
• and
• storing the synthetic time series dataset by combining the first plurality of data points, the second plurality of data points, and the third plurality of data points into corresponding time slots of the plurality of time slots. cm 14. The one or more non-transitory, computer-readable media of claim 13, wherein the instructions further cause the one or more processors to perform operations comprising:
• retrieving, from the user input, a percentage and a time period associated with the command;
• and
• determining, based on the percentage and the time period, a number of the one or more data points to update within the synthetic time series dataset.