Gaussian process imputation of multiple financial series
This work addresses the need for accurate imputation and prediction in financial signal processing, but it appears incremental as it builds on existing multi-output Gaussian process techniques.
The paper tackled the problem of jointly analyzing multiple financial time series by modeling them with a multi-output Gaussian process to learn market dependencies for imputation and prediction, and validated the model on two real-world datasets with comparisons to other methods.
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.