Multi-AI Exchange Rate Prediction System for Large Event Impact
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Solution Overview
Problem
Existing exchange rate prediction technologies are limited in their ability to predict short-term fluctuations by only using consistent algorithms without considering the duration of influence from large events.
Innovation Solution
The system extracts structured and unstructured data from sudden large events and builds multi-artificial intelligence models to predict short-term exchange rate fluctuations, allowing users to input specific data and models for personalized predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a consistent algorithm is used for exchange rate prediction, then the prediction method is simple and easy to implement, but the prediction accuracy for short-term fluctuations after large events is insufficient
Solution Approach 1:
The patent segments the prediction process into multiple stages by using different AI models for different prediction horizons. Specifically, it employs models suited for immediate impact prediction (0-1 day) and other models for gradual impact prediction (1-2 days), allowing each model to be optimized for its specific temporal scope rather than using a single universal model for all prediction needs.
Solution Approach 2:
The system dynamically selects and switches between different AI models based on the type of large event and the prediction requirements. The model selection is not fixed but adapts to the specific characteristics of each event, enabling the system to optimize prediction accuracy for different scenarios while maintaining manageable complexity through conditional model application.
2Measurement precision
If only structured data is used for prediction, then the data processing is straightforward, but the prediction cannot capture the full impact of sudden large events including psychological factors
Solution Approach 1:
The patent merges multiple data types - structured data (economic indicators, trade balances) and unstructured data (news articles, social media sentiment) - into a unified prediction framework. By combining these data sources, the system captures both quantitative economic factors and qualitative psychological factors, thereby improving prediction accuracy for the full impact of large events while managing complexity through integrated processing.
Solution Approach 2:
The system employs a universal data processing framework that can handle both structured and unstructured data through the same predictive models. This multi-functional approach allows the system to process diverse data types consistently, extracting meaningful features from both sources and using them together for comprehensive prediction without requiring separate processing pipelines for each data type.
3Adaptability or versatility
If the system considers the duration of influence from large events, then the prediction covers more comprehensive time frames, but the prediction for immediate short-term impact is diluted
Solution Approach 1:
The patent segments the prediction task by temporal scope, using different AI models for immediate impact (0-1 day) versus gradual impact (1-2 days). This segmentation allows the system to maintain high precision for short-term predictions by dedicating specific models to that timeframe, while still providing comprehensive coverage for longer-term predictions through separate specialized models.
Solution Approach 2:
The system applies local quality by tailoring the prediction approach to specific time horizons and event types. Different weights and parameters are applied to different data sources based on their relevance to immediate versus long-term impacts, allowing the system to optimize for short-term accuracy when needed while maintaining broader temporal coverage when appropriate.
Data Source
AI summary
An embodiment relates to an exchange rate prediction method based on multi artificial intelligence models performed by a server, the exchange rate prediction method includes providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value, performing learning by inputting the input structured data into the type of artificial intelligence model among multi artificial intelligence models, inputting current structured data into the learned model to calculate exchange rate prediction information, and providing the target exchange rate value and the calculated exchange rate prediction information, and receiving a correction value for the target exchange rate value from the user terminal.