Transaction Forecasting Using Multiple Time Dimensions
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Solution Overview
Problem
Current machine learning models face challenges in accurately forecasting financial transactions, particularly when dealing with prolonged sequences of inactivity followed by spikes in activity, leading to inefficient use of computing resources.
Innovation Solution
A transaction forecast system that processes historical data to train machine learning models using multiple time dimensions, generates clusters based on timing information, determines similarity scores, and selects the most probable time dimension for forecasting, thereby optimizing transaction forecasts and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple time dimensions and clustering techniques are used to improve forecast accuracy, then prediction precision improves, but computational complexity and resource consumption increase
Solution Approach 1:
The patent segments the transaction data into multiple clusters based on different time dimensions (daily, weekly, monthly patterns). By dividing the complex forecasting problem into smaller cluster-specific subproblems, the system can apply simplified modeling to each cluster rather than attempting to model all transactions uniformly, thus improving accuracy while managing computational complexity through divide-and-conquer.
Solution Approach 2:
The patent introduces multiple time dimensions (daily, weekly, monthly cycles) as additional analytical dimensions to the forecasting model. This dimensional expansion allows the system to capture seasonal and periodic patterns that single-dimensional models miss, improving forecast accuracy by analyzing transactions through multiple temporal lenses simultaneously.
2Reliability
If comprehensive historical data analysis across multiple time dimensions is performed, then forecast reliability improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary clustering of historical transaction data by time dimensions before executing the actual forecasting. By pre-organizing data into clusters based on daily, weekly, and monthly patterns, the system prepares the data structure in advance, enabling faster query and prediction operations when forecasts are needed, thus reducing real-time processing time while maintaining comprehensive analysis.
Solution Approach 2:
The patent applies different analysis methods and time dimension weights to different clusters of transactions based on their specific characteristics. Rather than applying a uniform complex analysis to all transactions, the system tailors the forecasting approach to each cluster's local patterns, improving reliability for each segment while reducing overall computational burden by avoiding unnecessary complexity for simpler clusters.
3Measurement precision
If multiple machine learning models are combined to improve prediction accuracy, then forecast precision improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent applies a selective approach where not all machine learning models are applied to all transaction types. Instead, the system identifies the most relevant time dimensions and models for each transaction cluster, applying only the necessary subset of models. This partial application strategy achieves sufficient prediction accuracy for each cluster without the excessive resource consumption of applying all possible models universally.
Data Source
AI summary
A device may configure a plurality of time dimensions of a forecast model to forecast transactions. The device may identify, using the forecast model and transaction information associated with forecasting a transaction, historical transaction data that includes timing information associated with executions of a set of previous transactions that are associated with the transaction. The device may generate, using the forecast model and the plurality of time dimensions, a plurality of clusters associated with the set of previous transactions. The device may determine, using the forecast model, similarity scores for the plurality of clusters that correspond to individual probabilities that the set of previous transactions occurred according to the plurality of time dimensions. The device may select, based on the similarity scores, a time dimension for a forecast of the transaction. The device may generate the forecast of the transaction based on the time dimension.