Dependent Mat\'ern Processes for Multivariate Time Series
For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Mat\'ern processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown values. Although similar models have been proposed in the...
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| Main Authors | , |
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| Format | Journal Article |
| Language | English |
| Published |
11.02.2015
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| Subjects | |
| Online Access | Get full text |
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| Summary: | For the challenging task of modeling multivariate time series, we propose a
new class of models that use dependent Mat\'ern processes to capture the
underlying structure of data, explain their interdependencies, and predict
their unknown values. Although similar models have been proposed in the
econometric, statistics, and machine learning literature, our approach has
several advantages that distinguish it from existing methods: 1) it is flexible
to provide high prediction accuracy, yet its complexity is controlled to avoid
overfitting; 2) its interpretability separates it from black-box methods; 3)
finally, its computational efficiency makes it scalable for high-dimensional
time series. In this paper, we use several simulated and real data sets to
illustrate these advantages. We will also briefly discuss some extensions of
our model. |
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| DOI: | 10.48550/arxiv.1502.03466 |