Systems and methods for improved precision in detecting false-positive changepoints in time-series data

A method for improved precision in detecting false-positive changepoints in time-series data, the method comprising:

• receiving a first dataset of time-series datapoints;
• determining a first changepoint in the first dataset;
• determining a first category of known values for the first dataset;
• based on the first category of the known values, selecting a first model from a plurality of models for determining whether the first changepoint corresponds to a first false-positive changepoint;
• in response to selecting the first model, processing, using the first model, the first changepoint and a first value of the known values to determine a first output;
• and
• generating for display, in a user interface, a first recommendation based on the first output, wherein the first recommendation indicates whether the first changepoint corresponds to the first false-positive changepoint.

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

• receiving a first dataset of time-series datapoints, wherein each datapoint in the time-series datapoints corresponds to a specific time stamp, and wherein the first dataset comprises a first changepoint;
• determining a first category of known values for the first dataset;
• based on the first category of the known values, selecting a first model from a plurality of models for determining whether the first changepoint corresponds to a first false-positive changepoint;
• in response to selecting the first model, processing, using the first model, the first changepoint and a first value of the known values to determine a first output;
• and
• generating a first recommendation based on the first output, wherein the first recommendation indicates whether the first changepoint corresponds to the first false-positive changepoint.