Abstract
We present a method for classifying multidimensional time series using concepts from nonlinear dynamical systems theory. Our contribution is an extension of support vector machines (SVM) that controls a nonlinear dynamical system. We use a chain of coupled Rössler oscillators with diffusive coupling to model highly nonlinear and chaotic time series. The optimization procedure involves alternating between using the sequential minimal optimization algorithm to solve the standard SVM dual problem and computing the solution of the ordinary differential equations defining the dynamical system. Empirical comparisons with kernel-based methods for time series classification on real data sets demonstrate the effectiveness of our approach.
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Huerta, R., Vembu, S., Muezzinoglu, M.K., Vergara, A. (2012). Dynamical SVM for Time Series Classification. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_22
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DOI: https://doi.org/10.1007/978-3-642-32717-9_22
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