System, Method, and Computer Program Product for Analyzing Multivariate Time Series Using a Convolutional Fourier Network

A method, comprising:

• receiving, with at least one processor, a time series of historical data points, wherein the historical data points include values of a plurality of transaction features with regard to a plurality of time intervals during an initial time period;
• determining, with at least one processor, a historical time period, wherein the historical time period comprises a first time period of the initial time period;
• determining, with at least one processor, a contemporary time period, wherein the contemporary time period comprises a second time period of the initial time period that is after the historical time period, wherein the historical time period is longer than the contemporary time period;
• determining, with at least one processor, a first time series of data points associated with a historical transaction metric from the historical time period;
• determining, with at least one processor, a second time series of data points associated with a historical target transaction metric from the historical time period;
• determining, with at least one processor, a third time series of data points associated with a contemporary transaction metric from the contemporary time period;
• generating, with at least one processor, a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric, wherein generating the machine learning model comprises:
• training the machine learning model based on the first time series of data points associated with the historical transaction metric, the second time series of data points associated with the historical target transaction metric, and the third time series of data points associated with the contemporary transaction metric;
• generating an output of the machine learning model, wherein the output of the machine learning model comprises the contemporary target transaction metric, and wherein the contemporary target transaction metric comprises a value of a target transaction metric during a targeted prediction period;
• providing the first time series of data points associated with the historical transaction metric, the second time series of data points associated with the historical target transaction metric, and the third time series of data points associated with the contemporary transaction metric as inputs to a processing layer of the machine learning model, wherein the processing layer comprises a fast Fourier transform (FFT) layer;
• providing an output of the processing layer as an input to a feature extraction component of the machine learning model;
• providing an output of the feature extraction component of the machine learning model as an input to a dual-attention component of the machine learning model;
• and
• providing an output of the dual-attention component of the machine learning model as an input to a learning and prediction component of the machine learning model.

A 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 time series of historical data points, wherein the historical data points include values of a plurality of transaction features with regard to a plurality of time intervals during an initial time period;
• determine a historical time period, wherein the historical time period comprises a first time period of the initial time period;
• determine a contemporary time period, wherein the contemporary time period comprises a second time period of the initial time period that is after the historical time period, wherein the historical time period is longer than the contemporary time period;
• determine a first time series of data points associated with a historical transaction metric from the historical time period;
• determine a second time series of data points associated with a historical target transaction metric from the historical time period;
• determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period;
• generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric, wherein, the one or more instructions that cause the at least one processor to generate the machine learning model, cause the at least one processor to:
• train the machine learning model based on the first time series of data points associated with the historical transaction metric, the second time series of data points associated with the historical target transaction metric, and the third time series of data points associated with the contemporary transaction metric;
• generate an output of the machine learning model, wherein the output of the machine learning model comprises the contemporary target transaction metric, and wherein the contemporary target transaction metric comprises a value of a target transaction metric during a targeted prediction period;
• provide the first time series of data points associated with the historical transaction metric, the second time series of data points associated with the historical target transaction metric, and the third time series of data points associated with the contemporary transaction metric as inputs to a processing layer of the machine learning model, wherein the processing layer comprises a fast Fourier transform (FFT) layer;
• provide an output of the processing layer as an input to a feature extraction component of the machine learning model;
• provide an output of the feature extraction component of the machine learning model as an input to a dual-attention component of the machine learning model;
• and
• provide an output of the dual-attention component of the machine learning model as an input to a learning and prediction component of the machine learning model.