Financial Time Series Diffusion for Irregular Scale-Invariant Patterns
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Solution Overview
Problem
Existing deep learning models struggle to generate synthetic financial time series (FTS) due to their irregular and scale-invariant nature, which complicates the identification of latent patterns and leads to overfitting, especially with limited data and high noise levels.
Innovation Solution
A novel FTS-Diffusion framework that decomposes the generation process into pattern recognition, generation, and evolution, using a SISC algorithm for identifying scale-invariant patterns, a pattern-conditioned diffusion network for segment synthesis, and a Markov transition estimator for temporal dynamics, leveraging DTW and DDPM for accurate and stable data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are trained on limited financial time series data, then model training can be performed, but the models are prone to overfitting and cannot perform reliably on unseen data
Solution Approach 1:
The patent uses generative models (VAEs, GANs, diffusion models) to create synthetic copies of financial time series data that preserve the statistical properties and patterns of real data. These synthetic data copies augment the limited real data available for training, enabling more robust model training without overfitting.
Solution Approach 2:
The patent transforms real financial time series data into synthetic data by changing parameters such as volatility, drift, and correlation structures while preserving the underlying market dynamics. This allows generation of diverse training samples that maintain realism while providing sufficient data quantity.
2Adaptability or versatility
If existing generative models are used to model financial time series, then data generation can be performed, but the models struggle with irregular and scale-invariant patterns central to FTS
Solution Approach 1:
The patent segments financial time series data into multiple scales (e.g., intra-day, daily, weekly, monthly patterns) and processes each scale separately through the generative model. This segmentation allows the model to capture irregular patterns at different temporal resolutions while maintaining overall coherence.
Solution Approach 2:
The patent introduces scale-invariance as an additional dimension in the generative process, allowing patterns to be generated that maintain their structural properties across different time scales. This is achieved through multi-scale feature extraction and generation that preserves self-similar characteristics inherent in financial data.
3Quantity of substance
If data augmentation techniques are employed to alleviate data scarcity, then synthetic data can be generated, but the techniques must handle high noise levels and extract useful information from limited datasets
Solution Approach 1:
The patent introduces latent space representations as intermediaries between real data and synthetic data generation. The generative models learn to map real data into latent representations that capture essential patterns while filtering out noise, then generate synthetic data from these cleaned latent representations.
Solution Approach 2:
The patent replaces traditional noise-filtering mechanical methods with learning-based denoising approaches where neural networks automatically learn to distinguish signal from noise during the generative process, substituting complex filtering mechanics with adaptive learning mechanisms.
Data Source
AI summary
Limited data availability poses a major obstacle in training deep learning models for financial applications. Synthesizing financial time series (FTS) to augment real-world data is challenging due to irregular and scale-invariant patterns associated with FTS—temporal dynamics that repeat with varying duration and magnitude. A novel generative framework called FTS-Diffusion is developed, consisting of three modules to model irregular and scale-invariant patterns. First, a scale-invariant pattern recognition algorithm extracts recurring patterns that vary in duration and magnitude. Second, a pattern-conditioned diffusion network synthesizes segments of patterns. Third, the temporal evolution of patterns is modeled in order to aggregate the generated segments. Extensive experiments show that FTS-Diffusion generates synthetic FTS highly resembling observed data, outperforming state-of-the-art alternatives. Two downstream experiments demonstrate that augmenting real-world data with synthetic data generated by FTS-Diffusion reduces the error of stock market prediction by up to 17.9%.