Deep Learning Financial Forecasting Model
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Solution Overview
Problem
Conventional techniques for financial time series forecasting, such as ARIMA and VAR, fail to adequately represent both internal patterns and external factors, leading to inaccurate predictions due to their linear nature and limited capability in handling non-linear distributions and long-term dependencies.
Innovation Solution
The implementation of a deep learning model, specifically a non-linear deep-SARIMAX model, that uses a neural network architecture to model internal patterns and external factors using a kernel function, allowing for dynamic parameter adjustment to improve forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear models (ARIMA, VAR) are used for financial time series forecasting, then the model structure is simple and easy to implement, but the forecasting accuracy deteriorates due to inability to capture non-linear patterns and long-term dependencies
Solution Approach 1:
The patent transforms the forecasting model from linear parameters to non-linear parameters by implementing a deep learning neural network. The model learns optimal non-linear parameters automatically from historical data, enabling it to capture complex non-linear patterns and long-term dependencies in financial time series that conventional linear models cannot represent
Solution Approach 2:
The patent replaces the mechanical linear mathematical framework (ARIMA, VAR) with a data-driven neural network system. This substitution allows the model to adaptively learn non-linear relationships from data rather than relying on pre-specified linear assumptions, significantly improving forecasting accuracy for complex financial patterns
2Measurement precision
If conventional linear models are used, then the model is easy to operate and interpret, but the representation of external factors and internal patterns is inadequate
Solution Approach 1:
The patent segments the forecasting model into distinct functional components: an embedding layer for external factors, a recurrent neural network for internal patterns, and an output layer for predictions. This segmentation allows each component to specialize in capturing specific aspects of the data, improving overall feature representation while maintaining a structured approach to model interpretation
3Measurement precision
If deep learning models with dynamic parameter adjustment are implemented, then forecasting accuracy improves by capturing non-linear patterns and external factors, but the computational resources and training time increase
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network model on historical financial data to learn optimal parameter configurations and non-linear patterns. This pre-learning process enables the model to make accurate forecasts with reduced computational requirements during actual deployment, as the heavy lifting of pattern recognition is completed during the offline training phase
Data Source
AI summary
The present disclosure describes techniques to forecast financial data using deep learning. These techniques are operative to transform time series data in a financial context into a machine learning model configured to predict future financial data. The machine learning model may implement a deep learning structure to account for a sequence-sequence prediction where a movement/distribution of the time series data is non-linear. The machine learning model may incorporate features related to one or more external factors affecting the future financial data. Other embodiments are described and claimed.