  In Financial Signal Processing, multiple time series such as financial
indicators, stock prices and exchange rates are strongly coupled due to their
dependence on the latent state of the market and therefore they are required to
be jointly analysed. We focus on learning the relationships among financial
time series by modelling them through a multi-output Gaussian process (MOGP)
with expressive covariance functions. Learning these market dependencies among
financial series is crucial for the imputation and prediction of financial
observations. The proposed model is validated experimentally on two real-world
financial datasets for which their correlations across channels are analysed.
We compare our model against other MOGPs and the independent Gaussian process
on real financial data.
