  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.
