Online Learning of Non-Stationary Networks, with Application to Financial Data
yasunori at cs.duke.edu
||Thursday, July 19, 2012
||3:00pm - 5:00pm
||D344 LSRC, Duke
||Ronald Parr, Uwe Ohler
In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied in Signal Processing and Computational Biology, those algorithms are based on batch learning algorithms that cannot be applied to online time-series data. Therefore, we propose a learning algorithm based on a Particle Filtering approach so that we can apply that algorithm to online time-series data. To evaluate our algorithm, we apply it to the simulated data set and the real-world financial data set. The result on the simulated data set shows that our algorithm performs accurately makes estimation and detects change. The result applying our algorithm to the real-world financial data set shows several features, which are suggested in previous research that also implies the effectiveness of our algorithm.
Advisor(s): Alexander Hartemink