Systems and methods for synthetic data generation for time-series data using data segments
Systems and methods for generating synthetic data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset including time-series data. The operations may include generating a plurality of data segments based on the dataset, determining respective segment parameters of the data segments, and determining respective distribution measures of the data segments. The operations may include training a parameter model to generate synthetic segment parameters. Training the parameter model may be based on the segment parameters. The operations may include training a distribution model to generate synthetic data segments. Training the distribution model may be based on the distribution measures and the segment parameters. The operations may include generating a synthetic dataset using the parameter model and the distribution model and storing the synthetic dataset.
1. 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 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 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 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.
2. The system of claim 1 , wherein generating the synthetic dataset comprises:
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.
3. The system of claim 1 , wherein:
the operations further comprise generating the parameter model; and
training the parameter model is based on generating the parameter model.
4. The system of claim 1 , wherein:
the operations further comprise generating the distribution model; and
training the distribution model is based on generating the distribution model.
5. The system of claim 1 , wherein generating data segments is based on a predetermined segment size.
6. The system of claim 1 , wherein generating data segments comprises determining a segment size based on a statistical measure of the dataset.
7. The system of claim 1 , wherein the segment parameters comprise a minimum value, a maximum value, a start value, and an end value.
8. The system of claim 1 , wherein the distribution measures include at least one of a variance, a standard deviation, or a regression result of a time-dependent function.
9. The system of claim 1 , wherein the parameter model comprises at least one of a recurrent neural network model or a long short-term memory model.
10. The system of claim 1 , wherein the distribution model comprises a multilayer perceptron model, a convolutional neural network model, or a sequence-to-sequence model.
11. The system of claim 1 , wherein training the distribution model comprises:
training the distribution model to generate synthetic segment-data;
determining synthetic distribution-measures of the synthetic data-segments;
determining a performance metric based on the distribution measures and the synthetic distribution-measures; and
terminating training of the distribution model based on the performance metric satisfying a criterion.
12. The system of claim 1 , wherein training the parameter model is based on an autocorrelation of the segment parameters.
13. The system of claim 1 , the operations further comprising:
generating a data profile of the dataset; and
storing the parameter model in a data index based on the data profile.
14. The system of claim 1 , the operations further comprising:
generating a data profile of the dataset; and
storing the distribution model in a data index based on the data profile.
15. The system of claim 1 , wherein:
the dataset comprises multidimensional time-series data;
the data segments comprise multidimensional data segments; and
the segment parameters comprise multidimensional segment parameters.
16. The system of claim 15 , wherein:
the operations further comprise determining a correlation of two or more dimensions of the segment parameters; and
training the parameter model is based on the correlation.
17. The system of claim 1 , wherein:
receiving the dataset comprises receiving the dataset from a client device; and
the operations further comprise transmitting the synthetic dataset to the client device.
18. The system of claim 1 , wherein receiving the dataset comprises receiving the dataset at a cloud service.
19. 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.
20. 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.