Systems and methods for synthetic data generation for time-series data using data segments

A method for generating synthetic data, the method comprising:

• receiving a dataset comprising time-series data;
• generating a plurality of data segments based on the dataset;
• determining respective segment parameters of the data segments;
• determining respective distribution measures of the data segments;
• training a parameter model to generate synthetic segment-parameters comprising a time series of bounding box parameters, the training being based on the segment parameters, wherein the bounding box parameters include a minimum value, a maximum value, a start value, and an end value;
• training a distribution model to generate synthetic data-segments, the training being based on the distribution measures and the data segment parameters;
• generating a synthetic dataset using the parameter model and the distribution model;
• storing the synthetic dataset;
• and
• determining performance of the parameter model based on the synthetic dataset.

A system for generating synthetic data, comprising:

• one or more memory units storing instructions;
• and
• one or more processors that execute the instructions to perform operations comprising:
• receiving a dataset comprising time-series data;
• generating a data profile of the dataset;
• generating a plurality of data segments based on the dataset;
• determining respective segment parameters of the data segments, the segment parameters comprising a minimum value, a maximum value, a start value, and an end value;
• determining respective distribution measures of the data segments;
• generating a parameter model based on the dataset;
• training the parameter model to generate synthetic segment-parameters comprising a time series of bounding box parameters, the training being based on the segment parameters, wherein the bounding box parameters include a minimum value, a maximum value, a start value, and an end value;
• generating a distribution model based on the dataset;
• training the distribution model to generate synthetic data-segments, the training being based on the distribution measures and the data segment parameters;
• generating a synthetic dataset by:
• generating, via the parameter model, a series of synthetic segment-parameters;
• and
• generating, via the distribution model, a series of synthetic data-segments based on the series of synthetic segment-parameters;
• storing the synthetic dataset;
• storing the parameter model and the distribution model in a data index based on the data profile;
• and
• determining performance of the parameter model based on the synthetic dataset.