Latent Space Time-Series Retrieval for Multi-Modal Financial Queries
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Solution Overview
Problem
Existing time-series databases do not efficiently capture and retrieve financial time-series data due to their rigid query formats and inability to handle the intrinsic complexity and retrieval modalities required by financial users, who need flexible query modalities for stylized facts like volatility and correlation.
Innovation Solution
A method using multi-modal time-series data retrieval through latent space projections, involving the generation of synthetic data with textual descriptions and images, training encoders to learn a shared latent space, and generating an index for efficient retrieval based on user queries, including text and image inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional SQL-style queries are used for time-series data retrieval, then the query format is fixed and simple to implement, but the system cannot handle the intrinsic complexity and diverse retrieval modalities required by financial users
Solution Approach 1:
The patent implements a universal retrieval system that accepts multiple query modalities (textual descriptions, images, sketches) and processes them through a unified latent space projection framework. The multi-modal encoder architecture enables the system to handle diverse input types uniformly, allowing financial users to query time-series data using their preferred modality without requiring separate processing pipelines for each type.
Solution Approach 2:
The patent introduces latent space projections as an intermediary representation that bridges different query modalities and the underlying time-series data. By projecting both queries and time-series data into a shared latent space, the system enables flexible retrieval without directly handling the complexity of each modality separately, thus achieving versatility while managing system complexity through the mediating latent representation.
2Reliability
If financial time-series data with complex statistical properties is stored, then the data captures real market behavior accurately, but users face difficulty expressing these statistical properties in rigid query formats
Solution Approach 1:
The patent transforms complex statistical properties of financial time-series data into a different parameter space - the latent space. Instead of requiring users to express queries in terms of raw statistical parameters (volatility, autocorrelation, etc.), the system projects both the data and queries into latent space where similarity can be measured directly, making operations easier while preserving data accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manually specifying statistical parameters in SQL queries with an automated semantic matching process. Users provide natural language or visual descriptions, and the system automatically matches them with time-series data having similar statistical properties through latent space comparison, eliminating the need for users to understand or specify complex statistical parameters.
3Quantity of substance
If massive heterogeneous datasets are stored to support comprehensive analysis, then the system can provide complete historical data, but retrieval speed and efficiency decrease
Solution Approach 1:
The patent extracts the essential semantic features of time-series data by projecting them into a compact latent space representation. This extraction process separates the critical retrieval-related information from the full data detail, enabling fast similarity search in the reduced latent space while the complete data remains stored for detailed analysis when needed.
Solution Approach 2:
The patent segments the retrieval process into two stages: (1) fast filtering in latent space using projected representations to identify candidate matches, and (2) detailed verification using the original full-resolution data. This segmentation allows the system to handle massive datasets efficiently by performing the computationally intensive operations only on a small subset of candidates rather than the entire dataset.
Data Source
AI summary
A method and a system for using multi-modal time-series data retrieval through latent space projections to efficiently capture and store time-series data are provided. The method includes: generating a synthetic set of time-series data and a corresponding set of textual descriptions; using the synthetic data to generate a corresponding set of images; using the images to train an image encoder to learn a first multi-modal shared latent space, and using the textual descriptions to train a text encoder to learn the shared latent space; receiving historical time-series data and storing the historical data in a database; generating an index that relates to the shared latent space and is usable for identifying information stored in the database; receiving a user query that relates to a first new set of time-series data; and retrieving, from the database, information that is responsive to the query.