Time Series and Sequential Data

Modelling time series and sequential data is an essential part of many different areas of science and engineering, including for example, signal processing and control, bioinformatics, speech recognition, econometrics and finance. Using basic building blocks such as hidden Markov models, linear Gaussian state-space models, and Bayesian networks, it is possible to develop sophisticated time series models for real world data. However learning (parameter inference / system identification) becomes computationally challenging for such sophisticated models.

See also Gaussian processes

Some relevant publications: