Time Series Image Conversion for Trading Forecast Accuracy
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Solution Overview
Problem
Conventional algorithms process time-series data as numerical data, failing to accurately capture human trader decisions based on graphical representations, which limits their ability to forecast future values effectively.
Innovation Solution
The method involves converting time-series data into images, using algorithms like Bollinger Bands, MACD, and RSI to identify patterns, and employing machine learning models to forecast future values as pixelated information, allowing for automated buy and sell recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms process time-series data as numerical data, then the processing is computationally efficient, but the accuracy of capturing human trader decisions and forecasting future values deteriorates
Solution Approach 1:
The patent creates visual copies of time-series data by converting numerical data into graphical representations (charts, plots, or images) that mimic how human traders actually view and interpret the data. This visual copying allows machine learning models to process the data in a format that preserves the visual patterns and trends that humans recognize, thereby improving the accuracy of capturing human trader decisions while maintaining computational efficiency through automated image processing techniques.
2Measurement precision
If time-series data is converted into images for processing, then the ability to capture human trader decisions improves, but the computational processing time and complexity increase
Solution Approach 1:
The patent applies preliminary actions by pre-processing and standardizing the conversion of time-series data into consistent visual formats before feeding them to machine learning models. This includes normalizing data ranges, standardizing chart types, and pre-identifying key visual patterns. By performing these preparatory steps in advance, the system reduces the computational burden during actual forecasting operations, thereby improving processing efficiency while maintaining the accuracy benefits of visual data representation.
3Adaptability or versatility
If visual patterns are used for trading decisions, then the identification of trading opportunities improves, but the automation of the decision-making process becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models are trained on historical visual data with known trading outcomes, allowing the system to learn from past decisions and continuously improve its pattern recognition capabilities. The system provides feedback by comparing predicted trading opportunities against actual market outcomes, adjusting its visual pattern recognition algorithms accordingly. This feedback loop enables the system to maintain high adaptability in identifying diverse trading opportunities while achieving a high degree of automation in the decision-making process.
Data Source
AI summary
A method for using images that represent time-series data to forecast future images depicting future values as pixelated information is provided. The method includes: receiving a first set of time-series data; converting the received first set of time-series data into a first image; and using the first image to forecast a future image depicting future values as pixelated information that corresponds to a future time interval Training sets of time-series data are used to generate historical data that provides input to a machine learning algorithm, which provides, as an output, a composite image that depicts the future values as pixelated information that reflects associated uncertainties in the value predictions.