Patent ID: 11922290
Assignee: VISA INTERNATIONAL SERVICE ASSOCIATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system, comprising:
at least one processor programmed or configured 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 first time series of data points associated with a historical transaction metric and a second time series of data points associated with a historical target transaction metric from a historical time period, wherein the historical time period comprises a first time period of the initial time period;
determine a third time series of data points associated with a contemporary transaction metric from 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, and wherein the historical time period is longer than the contemporary time period; and
train 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 contemporary target transaction metric comprises a value of a target transaction metric during a targeted prediction period, and wherein, when training the machine learning model, the at least one processor is programmed or configured to:
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, the second time series of data points, and the third time series of data points 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,
wherein, when providing the first time series of data points, the second time series of data points, and the third time series of data points as inputs to the processing layer, the at least one processor is programmed or configured to:
provide each feature of a first plurality of features of the first time series of data points as an input to the FFT layer to generate a real part and an imaginary part for each feature of the first plurality of features;
provide each feature of a second plurality of features of the second time series of data points as an input to the FFT layer to generate a real part and an imaginary part for each feature of the second plurality of features; and
provide each feature of a third plurality of features of the third time series of data points as an input to the FFT layer to generate a real part and an imaginary part for each feature of the third plurality of features;

combine the real part for each feature of the first plurality of features to generate a combined real part of the first plurality of features;
combine the imaginary part for each feature of the first plurality of features to generate a combined imaginary part of the first plurality of features;
combine the real part for each feature of the second plurality of features to generate a combined real part of the second plurality of features:
combine the imaginary part for each feature of the second plurality of features to generate a combined imaginary part of the second plurality of features;
combine the real part for each feature of the third plurality of features to generate a combined real part of the third plurality of features; and
combine the imaginary part for each feature of the third plurality of features to generate a combined imaginary part of the third plurality of features;
wherein the output of the processing layer of the machine learning model comprises the combined real part of the first plurality of features, the combined imaginary part of the first plurality of features, the combined real part of the second plurality of features, the combined imaginary part of the second plurality of features, the combined real part of the third plurality of features, and the combined imaginary part of the third plurality of features.