Combined Prediction Model for Financial Forecasting Drift
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Solution Overview
Problem
Conventional systems for forecasting account balances suffer from errors that accumulate over time, leading to inaccurate predictions due to the inability to account for incremental errors and lack of infrastructure for large-scale, multi-step, multi-variate, and multi-entity forecasts, resulting in a 'drift' issue where forecasted values deviate significantly from actual balances.
Innovation Solution
A system and method utilizing a combined prediction model that learns to forecast future inflow and outflow activities by optimizing an objective function to penalize errors, incorporating historical data, and adjusting weight parameters to reduce drift, enabling multi-step, multi-variate, and multi-entity forecasting across multiple accounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional forecasting systems are used, then simple single-step forecasts can be generated, but errors accumulate over time leading to inaccurate multi-step forecasts
Solution Approach 1:
The forecasting system is divided into separate models for inflow and outflow predictions, each trained on specific historical data patterns. This segmentation allows each model to specialize in its specific prediction task, reducing error accumulation compared to a single monolithic forecast model.
Solution Approach 2:
The system incorporates feedback mechanisms where actual account balances are compared with forecasted balances, and the difference (drift) is used to adjust and retrain the prediction models. This continuous feedback loop ensures that models adapt to changing patterns and reduce cumulative errors over time.
2Adaptability or versatility
If existing forecasting solutions are used, then basic account balance predictions can be made, but they lack infrastructure for large-scale multi-step, multi-variate and multi-entity forecasts
Solution Approach 1:
The system employs a unified forecasting framework that can handle multiple forecasting scenarios simultaneously - single-step and multi-step forecasts, single-entity and multi-entity forecasts, and univariate and multivariate forecasts. This universal approach allows the same infrastructure to serve diverse forecasting needs without requiring separate specialized systems.
Solution Approach 2:
The system transitions from traditional single-dimensional forecasting to multi-dimensional forecasting by simultaneously predicting multiple variables (inflow, outflow, balance), multiple time steps, and multiple entities within a single integrated framework. This dimensional expansion enables comprehensive analysis without proportionally increasing system complexity.
3Manufacturing precision
If forecast errors are not penalized, then predictions can be generated quickly, but drift occurs causing significant deviation from actual balances over time
Solution Approach 1:
The objective function incorporates penalty terms that measure the deviation between forecasted and actual balances, creating a feedback mechanism during training. This ensures that models learn to minimize drift and improve prediction precision by continuously adjusting their parameters based on actual performance metrics.
Solution Approach 2:
The system dynamically adjusts model parameters and hyperparameters during training to optimize the balance between prediction precision and training efficiency. By changing parameters such as learning rates, penalty weights, and model architecture configurations, the system achieves high precision without excessive training time.
Data Source
AI summary
A system and method are disclosed herein. The system includes one or more processors and a memory having programming instructions stored thereon, which, when executed by the one or more processors, performs operations. The operations include retrieving historical account activity. The operations further include constructing a training data set that includes the historical inflow data, the historical outflow data, and known forecast information from the historical account activity. The operations further include generating a combined prediction model configured to forecast future inflow activity and future outflow activity. The operations further include receiving current inflow activity, current outflow activity, and current balance information for a user. The operations further include generating a predicted account balance by forecasting, by the prediction model, a future inflow and a future outflow and constructing the predicted account balance based on the future inflow, the future outflow, and the current balance information.