Financial Chart Image Prediction Device Reducing Hardware Load
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Solution Overview
Problem
Existing methods for predicting financial instrument prices, such as those using artificial intelligence, require extensive data input, leading to a significant hardware resource load due to the need for processing time-series chart data.
Innovation Solution
A prediction device and method that analyzes chart images of financial instrument price fluctuations by acquiring and processing image data, using an image processing unit to identify trend and non-trend elements, and inputs this data into a learned model to predict future prices, thereby reducing the hardware burden and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all time-series chart data is input to a machine learning model, then prediction accuracy is improved, but hardware resource load increases significantly
Solution Approach 1:
The patent extracts only the essential features from chart images (trend elements, pattern types, key price points) rather than inputting all raw time-series data. The image processing unit identifies and extracts relevant visual features, which are then fed to the machine learning model, significantly reducing data volume while maintaining prediction accuracy.
Solution Approach 2:
The patent segments the chart image analysis into distinct processing stages: image acquisition, feature identification (trend elements), pattern type classification, and prediction. This segmentation allows selective processing of only relevant features rather than analyzing all data points, reducing computational burden on hardware resources.
2Reliability
If extensive data from multiple systems is input to predict price fluctuations, then prediction reliability is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources (chart images, price data, time information) into a unified image-based analysis framework. By converting various data types into visual chart representations that can be processed together through image recognition and feature extraction, the system simplifies the integration of multi-source data while maintaining comprehensive analysis capability.
3Loss of information
If detailed chart analysis is performed to identify investment features, then user investment skill development is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and pattern type classification before final prediction. The image processing unit pre-identifies trend elements and chart patterns, organizing this information in advance so that the machine learning model receives pre-processed, structured features. This preliminary action reduces the time required for the main prediction process while preserving all necessary investment feature information.
Data Source
AI summary
There is provided a prediction device that analyzes a chart image indicating price fluctuations of a financial instrument and predicts a future price of the financial instrument, the prediction device including an image acquiring unit that acquires a prediction target image to be a prediction target of the future price, an outputting unit that inputs the prediction target image to a learned model and outputs type data serving as a type of a chart included in the prediction target image from the learned model, and a predicting unit that outputs a predicted value of the future price based on a price after a lapse of a predetermined period from the type data.