System, Method, and Computer Program Product for Time Series Based Machine Learning Model Reduction Strategy

A method for generating a machine learning model based on encoded time series data using model reduction techniques, the method comprising:

• receiving, with at least one processor, a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points;
• performing, with the at least one processor, an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset;
• generating, with the at least one processor, one or more prediction models based on the encoded dataset, wherein the one or more prediction models are configured to provide an output in the lower dimension space;
• determining, with the at least one processor, an output of the one or more prediction models in the lower dimension space based on an input provided to the one or more prediction models;
• and
• performing, with the at least one processor, a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset.

A computer program product for generating a machine learning model based on encoded time series data using model reduction techniques, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:

• receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points;
• perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset;
• generate one or more prediction models based on the encoded dataset, wherein the one or more prediction models are configured to provide an output in the lower dimension space;
• determine an output of the one or more prediction models in the lower dimension space based on an input provided to the one or more prediction models;
• and
• perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset.