Income Distribution Forecasting With Demographic Data Harmonization

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Solution Overview

Problem

Current systems lack an accurate method to forecast income and expenditure distributions for countries, which is essential for financial planning and economic stability.

Innovation Solution

A system comprising a prediction and forecast electronic device with an income and expenditure per household engine, a distribution of income and expenditures engine, and a merger engine, which obtains data from sources like IMF, WB, and OECD, harmonizes and breaks down data into percentage steps, applies demographic distributions, and uses machine learning to forecast income and expenditure distributions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If current systems are used for financial forecasting, then basic budget prediction is possible, but accurate forecasting of income and expenditure distributions for specific demographics cannot be achieved

Engineering Contradiction:
Improveforecasting accuracyVSAvoiddemographic distribution details
Core Design Contradiction:
Measurement precisionVSLoss of information

Solution Approach 1:

The system segments the population into specific demographic groups (age, gender, location) and breaks down income and expenditure data into percentage steps corresponding to each segment. This allows accurate forecasting for specific demographics while maintaining overall distribution patterns, resolving the contradiction between forecasting accuracy and demographic detail retention.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system adds demographic dimensions (age, gender, location) to the traditional income-expenditure forecasting framework. By incorporating these additional dimensions through multi-dimensional data structures and machine learning models, the system achieves accurate forecasting that accounts for demographic variations without losing essential distribution information.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

2Adaptability or versatility

If data from multiple sources like IMF, WB, and OECD is collected and harmonized, then comprehensive forecasting capability is achieved, but system complexity increases

Engineering Contradiction:
Improvedata source compatibilityVSAvoiddata harmonization process
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system introduces a data harmonization layer that acts as an intermediary between multiple data sources (IMF, WB, OECD) and the forecasting model. This harmonization layer standardizes data formats, handles currency conversions, and aligns demographic classifications, enabling versatile data source compatibility while managing complexity through a dedicated processing layer.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates a universal data processing framework that can handle multiple data sources with different formats and structures. The harmonization engine serves multiple functions: data validation, currency conversion, demographic alignment, and format standardization, reducing overall system complexity by consolidating these functions into a single multi-functional component.

Inventive Principle:
Principle #6Universality (Multi-functionality)

3Measurement precision

If income and expenditure data is broken down into percentage steps for distribution analysis, then demographic breakdown accuracy is improved, but data processing complexity increases

Engineering Contradiction:
Improvedistribution breakdown precisionVSAvoiddata processing steps
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The system transforms absolute income and expenditure values into percentage steps representing distribution patterns. By changing the parameter representation from absolute values to relative percentages, the system achieves precise demographic breakdowns while simplifying comparisons across different regions and time periods. The machine learning model processes these percentage steps efficiently, managing processing complexity through optimized algorithms.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS20260073416A1System and method to forecast income and expenditures distributions
Publication Date: 2026.03.12 WORLD DATA LAB WDL GMBH
  • US20260073416A1 patent drawing
  • US20260073416A1 patent drawing
  • US20260073416A1 patent drawing

AI summary

Particular example embodiments described herein can provide for a system, an apparatus, and a method for collecting household income and expenditures data for a country, extending the household income and expenditures data for the country for each year in a period of time, collecting distribution demographic data for the country, extending the distribution demographic data for the country for each year in the period of time, and merging the household income and expenditures data and the distribution demographic data for the country to create a report forecasting income and expenditures distributions for the country.