Cash Flow Forecasting via Machine Learning Time Granularity Conversion
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Solution Overview
Problem
Existing methods for predicting cash flow are ineffective with sparse data sets, lacking sufficient information to identify patterns, trends, and factors contributing to cash flow fluctuations, and are prone to inconsistencies and unforeseen circumstances.
Innovation Solution
A machine-learning based computing method and system that generates forecasted cash flow data by receiving user inputs, converting month-level data to week-level and day-level data using a regression-based machine learning model, and dynamically assigning weightages to historical cash flow data and growth factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction methods (rolling average, Prophet models) are used, then they work well with dense data sets, but they fail to accurately predict cash flow with sparse data sets
Solution Approach 1:
The patent transforms the prediction approach by changing parameters from traditional statistical methods to machine learning models that can adapt to sparse data conditions. The system dynamically adjusts model parameters based on data density, using techniques like feature engineering and hyperparameter optimization to maintain prediction accuracy regardless of data quantity.
Solution Approach 2:
The patent introduces intermediary components including feature engineering layers and data preprocessing modules that act as mediators between the input sparse data and the prediction model. These intermediaries transform and enrich the sparse input data, extracting meaningful patterns that enable accurate predictions even with limited data availability.
2Ease of operation
If sparse data sets are used, then data collection requirements are reduced, but the system cannot identify patterns, trends, and factors contributing to cash flow fluctuations
Solution Approach 1:
The patent applies preliminary action through extensive feature engineering and data preprocessing steps performed before the actual prediction. The system pre-processes sparse data by imputing missing values, creating derived features, and engineering domain-specific features that capture potential patterns, thereby compensating for the lack of raw data volume.
Solution Approach 2:
The patent transitions from analyzing data in the time domain to incorporating multiple dimensions including feature space transformations. By projecting sparse temporal data into higher-dimensional feature spaces using machine learning techniques, the system recovers lost information and identifies patterns that are not apparent in the original sparse dataset.
3Adaptability or versatility
If sparse data sets with gaps or inaccuracies are used, then data completeness requirements are relaxed, but predictions become inconsistent or unreliable
Solution Approach 1:
The patent implements beforehand cushioning through robust error handling, data validation layers, and uncertainty quantification mechanisms. The system prepares for potential data quality issues by incorporating confidence intervals, prediction intervals, and anomaly detection that cushion against the impact of gaps and inaccuracies in sparse data, maintaining prediction reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where prediction outcomes are continuously evaluated and used to refine the model. The system uses feedback loops to adjust to data quality variations, learning from past predictions and improving its ability to handle sparse and inconsistent data, thereby enhancing prediction consistency over time.
Data Source
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AI summary
A machine learning based computing method for distributing financial transactions is disclosed. The machine learning based computing method includes steps of: receiving one or more user inputs from one or more users; generating one or more data from the one or more inputs received from the one or more users; converting the month level forecast cash flow data to the week level forecast cash flow data using a machine learning model; converting the week level forecast cash flow data to day level forecast cash flow data using the machine learning model; and providing an output of at least one of: the month level forecast cash flow data, the week level forecast cash flow data, and the day level forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices.