Convolutional Fourier Network for Multivariate Time Series Prediction
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Solution Overview
Problem
Existing predictive machine learning models for payment processing networks face challenges in real-time data processing due to latency in transaction data availability and are ineffective in handling multivariate time-series data with seasonality, leading to inaccurate predictions and delayed detection of network disruptions or fraudulent activities.
Innovation Solution
A convolutional Fourier network is employed to process historical and contemporary transaction data, utilizing a Fast Fourier Transform (FFT) layer, dual-attention mechanisms, and feature extraction convolutional layers to generate accurate predictions of transaction metrics, addressing latency and seasonality issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for payment transaction prediction, then the system can process transactions, but the latency in transaction data availability causes delayed detection of network disruptions and fraudulent activities
Solution Approach 1:
The system performs preliminary actions by continuously pre-processing and storing transaction data in real-time streams before prediction is needed. The convolutional Fourier network is pre-trained on historical data to establish baseline patterns, enabling immediate detection when new data arrives without waiting for complete data aggregation
Solution Approach 2:
The convolutional Fourier network acts as an intermediary between raw transaction data and prediction outputs. It transforms latency-prone raw transaction streams into frequency-domain representations that reveal patterns faster, mediating between incomplete data and reliable predictions
2Measurement precision
If the system waits for all transaction data to be ready for prediction, then prediction accuracy improves, but the system cannot keep up with real-time payment transaction dynamics
Solution Approach 1:
The system applies partial action by making predictions on partial data streams rather than waiting for complete datasets. The convolutional Fourier network processes available transaction windows incrementally, generating predictions even when data is incomplete, and refines predictions as more data becomes available
Solution Approach 2:
The transaction data stream is segmented into overlapping windows that can be processed independently and in parallel. The convolutional Fourier network analyzes each segment separately, enabling real-time processing while maintaining overall prediction accuracy through cumulative results
3Reliability
If the predictive model uses historical transaction data with seasonality patterns, then it can capture periodic behaviors, but it becomes ineffective at detecting sudden concept drift caused by external factors like economy, geopolitics, and pandemics
Solution Approach 1:
The system dynamically adapts by continuously retraining the convolutional Fourier network on incoming data streams. The model transitions from static seasonal pattern recognition to dynamic adaptation, adjusting its frequency responses to capture emerging patterns while retaining knowledge of historical seasonality
Solution Approach 2:
The system implements feedback mechanisms where prediction residuals and anomaly detections trigger model retraining and parameter adjustments. When concept drift is detected through feedback from unexpected patterns, the convolutional Fourier network adapts its filters to accommodate new behavioral regimes
4Loss of information
If the system processes multivariate time-series data with multiple transaction metrics, then comprehensive analysis is achieved, but the complexity of learning patterns from simultaneously received metrics increases significantly
Solution Approach 1:
The system transforms the complexity problem by changing dimensions - converting temporal patterns into frequency-domain representations through Fourier transformation. This dimensional change separates intertwined time-series patterns into distinct frequency components, making multivariate pattern learning more tractable
Solution Approach 2:
The convolutional Fourier network implements universal pattern recognition that works across different transaction metrics and time scales. The same architectural components handle diverse input types uniformly, reducing overall system complexity despite processing multiple metrics simultaneously
Data Source
AI summary
Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a 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, and 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. Methods and computer program products are also provided.