Demand Prediction Device Using Relevance-Based Index Filtering
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Solution Overview
Problem
Existing demand prediction devices may produce inaccurate predictions when including economic indexes with low relevance to product demand, leading to deviations from actual future demand.
Innovation Solution
A demand prediction device that acquires and processes temporal demand data and index candidate data, calculates relevance degrees, extracts relevant index data, and performs demand prediction using a selected prediction model to exclude low-relevance indexes, thereby preventing prediction deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple index candidate data including low relevance indexes are provided to the demand prediction device, then the comprehensiveness of input data is improved, but the prediction accuracy deteriorates due to inclusion of irrelevant information
Solution Approach 1:
The patent segments the input index candidate data into multiple groups based on their relevance to demand prediction. The processing circuitry divides the comprehensive index data set into relevant and irrelevant portions, allowing the system to utilize diverse data sources while filtering out low-relevance indexes that would otherwise degrade prediction accuracy.
Solution Approach 2:
The patent extracts and removes low-relevance index data from the input set before feeding data to the prediction model. The processing circuitry identifies and extracts only those index candidate data that have high relevance to demand prediction, thereby maintaining prediction accuracy while still considering multiple data sources.
2Adaptability or versatility
If all index candidate data are used for demand prediction, then the coverage of economic factors is improved, but the reliability of prediction result deteriorates due to noise from low relevance indexes
Solution Approach 1:
The patent performs preliminary filtering and relevance assessment of index candidate data before the actual demand prediction process. The processing circuitry pre-evaluates each index candidate data's relevance to demand prediction and prepares a filtered set of high-relevance indexes, ensuring that only reliable data enters the prediction model.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously evaluates the relevance of index candidate data based on their correlation with actual demand patterns. This feedback loop allows the system to adjust which indexes are considered relevant, improving prediction reliability while maintaining comprehensive economic factor coverage.
3Adaptability or versatility
If comprehensive index candidate data including irrelevant indexes are input to the analytical model, then the breadth of analysis is improved, but the prediction result deviates from actual future demand
Solution Approach 1:
The patent employs dynamic relevance assessment where the importance of each index candidate data is not fixed but adjusts based on current market conditions and historical patterns. The processing circuitry dynamically determines which indexes are relevant at any given time, allowing the system to maintain broad analytical coverage while adapting to changing conditions that affect prediction accuracy.
Data Source
AI summary
A demand prediction device includes processing circuitry configured to; acquire demand data indicating a temporal change of a past demand in a product of a demand prediction target and index candidate data indicating each of a plurality of indexes which are candidates of an index related to the past demand; calculate a relevance degree between at least one index indicated by each of index candidate data having been acquired and a demand indicated by the demand data having been acquired; extract index data used for demand prediction processing for predicting a future demand of the product from among the plurality of index candidate data having been acquired on a basis of the calculated relevance degree; perform the demand prediction processing using the extracted index data.