Weighted Data Quantization for Real-Time Financial Forecasting
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Solution Overview
Problem
Existing financial data processing systems lack the technical sophistication to efficiently handle large-scale, heterogeneous data while maintaining prediction accuracy, particularly in real-time financial forecasting, due to inadequate methods for converting raw data into quantifiable forms and assigning differentiated weight values to historical and real-time inputs.
Innovation Solution
A data quantization method that collects definite and estimated values across predefined periods, calculates comparison reference values, and assigns weights to these values to generate quantized data graphs, facilitating data management and analysis by integrating association data for improved accessibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static statistical models are used for financial forecasting, then the system is simple to implement, but the prediction accuracy is low due to inability to handle heterogeneous data and assign differentiated weight values
Solution Approach 1:
The patent segments financial data into multiple dimensions including time series data, cross-sectional data, and hierarchical structures. It divides data processing into separate modules for collecting definite values, estimated values, and weight values, allowing each segment to be processed with appropriate methods while maintaining overall prediction accuracy.
Solution Approach 2:
The patent applies local quality by assigning different weight values to different data points based on their relevance, timeliness, and reliability. It uses dynamic weighting mechanisms where important historical periods and recent data receive higher weights, while less relevant data receives lower weights, enabling differentiated treatment of heterogeneous data.
2Measurement precision
If all input variables are treated equally in financial forecasting models, then the model is simple to implement, but the prediction results are inaccurate due to overlooking dynamic and nonlinear influence of market factors
Solution Approach 1:
The patent dynamically changes parameters by adjusting weight values assigned to different input variables based on their current relevance and historical performance. It implements time-varying parameters where the importance of different market factors changes over time, allowing the model to adapt to dynamic market conditions without requiring complex restructure.
Solution Approach 2:
The patent introduces dynamics into the forecasting model by using moving averages, rolling windows, and adaptive weighting schemes that automatically adjust to changing market conditions. The system continuously updates weight values based on recent performance, making the model responsive to nonlinear market dynamics without requiring manual intervention.
3Measurement precision
If complex data structures are used to capture dynamic market factors, then the prediction accuracy improves, but the system becomes difficult to operate and analyze
Solution Approach 1:
The patent introduces intermediary structures such as aggregate indicators, summary statistics, and standardized weight value scales that bridge complex data structures and user-friendly interfaces. These intermediaries transform complex heterogeneous data into simplified representations that maintain predictive power while being easier to interpret and operate with.
Solution Approach 2:
The patent creates simplified copies or representations of complex data structures, such as using summary statistics to represent detailed time series data, or using standardized indices to represent multiple market factors. These copies preserve the essential information needed for prediction while reducing operational complexity and improving accessibility.
Data Source
AI summary
A data quantization apparatus collecting data corresponding to a pre-set definite value for each period of a previous section pre-set on the basis of a current date, an estimated value and definite value for a current year on the basis of the current date, and an estimated value for each period of a subsequent section pre-set based on the current date; calculating at least one comparison reference value on the basis of a provision value after collecting the provision value that can be compared with the estimated value and the definite value; and quantizing the data for each period and for each section via a method for assigning a weight to the current year for each period, and each period of the previous section and subsequent section, by comparing the definite value for each period of the previous section, the definite value for each section of the current year, and the estimated value for each period of the subsequent section with the comparison reference value.