Forecast Support Device Model Training for Disclosure Impact
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing forecasting technologies fail to accurately reflect the impact of disclosed forecast values on actual values, leading to suboptimal predictions in various domains such as traffic, finance, and health, as they do not account for the behavioral changes induced by forecast disclosure.
Innovation Solution
A forecast support device and method that acquires learning data including both forecast and actual values when a forecast is made, and trains a model to indicate the relationship between these values, allowing the impact of forecast disclosure to be reflected in subsequent forecasts, thereby improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used, then the forecasting process is simple, but the forecast accuracy is insufficient because the impact of forecast disclosure on actual values is not reflected
Solution Approach 1:
The system performs preliminary actions by collecting historical data on forecast disclosures and actual values before conducting new forecasts. It pre-trains a machine learning model on this historical data to capture the relationship between forecast disclosures and actual outcomes, enabling the system to account for disclosure effects in advance rather than calculating them in real-time during forecasting
Solution Approach 2:
The system implements feedback by using historical forecast-actual value pairs to train a machine learning model that captures how forecast disclosures influence actual outcomes. This trained model then provides feedback adjustments to new forecasts, allowing the system to iteratively improve accuracy by learning from past disclosure effects and applying those lessons to future predictions
2Measurement precision
If the impact of forecast disclosure is reflected in forecasts, then forecast accuracy improves, but data requirements and processing complexity increase
Solution Approach 1:
The system collects and stores historical forecast disclosure data and actual value data in advance, building a comprehensive historical database before conducting new forecasts. This preliminary data accumulation enables the machine learning model to learn from extensive historical patterns without requiring large volumes of data to be processed in real-time
Solution Approach 2:
The system uses a machine learning model as a virtual copy or surrogate that replicates the complex relationship between forecast disclosures and actual values. Instead of directly analyzing raw historical data during forecasting, the trained model serves as a compressed representation that captures essential patterns, reducing the computational burden of processing large data volumes while maintaining forecast accuracy
Data Source
AI summary
A forecast support device acquires learning data including: a forecast value; and an actual value when the forecast value is disclosed. The forecast support device trains a model indicating a relationship between: the forecast value; and the actual value when the forecast value is disclosed, by using the learning data.