Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections

TL;DR

This paper introduces a deep learning framework that encodes financial time-series data into a lower-dimensional latent space, enabling efficient retrieval using natural language or sketches, and capturing complex data properties beyond traditional query formats.

Contribution

The authors propose a novel multi-modal latent space encoding for financial time-series that improves retrieval efficiency and supports intuitive query modalities like language and sketches.

Findings

Enhanced retrieval accuracy on real and synthetic data

Reduced computational complexity in data storage and search

Supports natural language and sketch-based queries effectively

Abstract

Financial firms commonly process and store billions of time-series data, generated continuously and at a high frequency. To support efficient data storage and retrieval, specialized time-series databases and systems have emerged. These databases support indexing and querying of time-series by a constrained Structured Query Language(SQL)-like format to enable queries like "Stocks with monthly price returns greater than 5%", and expressed in rigid formats. However, such queries do not capture the intrinsic complexity of high dimensional time-series data, which can often be better described by images or language (e.g., "A stock in low volatility regime"). Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial. In this paper, we propose and demonstrate a framework to store multi-modal data for financial time-series in a lower-dimensional latent space using deep encoders, such that the latent space projections capture not only the time series trends but also other desirable information or properties of the financial time-series data (such as price volatility). Moreover, our approach allows user-friendly query interfaces, enabling natural language text or sketches of time-series, for which we have developed intuitive interfaces. We demonstrate the advantages of our method in terms of computational efficiency and accuracy on real historical data as well as synthetic data, and highlight the utility of latent-space projections in the storage and retrieval of financial time-series data with intuitive query modalities.