Quantitative Model Selection for Heterogeneous Time Series Learning

W. Hsu and S. Ray

A key benefit of modular learning is the ability to apply different algorithms to suit the characteristics of each subtask. This approach requires methods for task decomposition, classifier fusion, and matching of subproblems to learning techniques. In this paper, we present a new method for technique selection from a "repertoire" of statistical learning architectures (specifically, artificial neural networks and Bayesian networks) and methods (Bayesian learning, mixture models, and gradient learning). We first discuss the problem of learning heterogeneous time series, such as sensor data from multiple modalities. We then explain how to construct composite learning systems by selecting model components. Finally, we outline the design of a composite learning system for geospatial monitoring problems and present an application (precision agriculture) that demonstrates its potential benefits.


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