Learning kernels for time sequences classification

In this paper we consider a direct application of advanced machine learning methods to standard model of time sequences data to avoid preprocessing. Besides classical machine learning methods, such as support vector machines, we used kernel learning to improve accuracy of learned knowledge. Kernel learning, especially multiple kernel learning (MKL), allows automated model creation to describe complex data and performs feature selection. The approach is tested using several publicly available machine learning software tools and time series datasets and its good generalization properties are demonstrated.