Discovering Explainable Latent Covariance Structure for Multiple Time Series
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Analyzing time series data is important to predict future events and changes in finance, manufacturing, and administrative decisions. Gaussian processes (GPs) solve regression and classification problems by choosing appropriate kernels capturing covariance structure of data. In time series analysis, GP based regression methods recently demonstrate competitive performance by decomposing temporal covariance structure. Such covariance structure decomposition allows exploiting shared parameters over a set of multiple but selected time series. In this paper, we handle multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels. We investigate the validity of model when infinite latent components are introduced. We also propose an improved search algorithm to find interpretable kernels among multiple time series along with comparison reports. Experiments are conducted on both synthetic data sets and real world data sets, showing promising results in term of structure discoveries and predictive performances.
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