Online Learning of Non-Stationary Networks, with Application to Financial Data

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Date

2012

Authors

Hongo, Yasunori

Advisors

Hartemink, Alexander J

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Abstract

In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks is proposed. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied in Signal Pro- cessing 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.

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Citation

Hongo, Yasunori (2012). Online Learning of Non-Stationary Networks, with Application to Financial Data. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/5839.

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