Classification, Similarity Search, and Labeling of Time Series Data

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1. We have developed kernels for time series data using dynamic time warping (DTW) distances. We use semidefinite programming to guarantee the positive definiteness of a kernel matrix. We use two applications, time series classification and time series embedding for similarity search to validate our approach.2. We have proposed HHCRFs (hierarchical hidden CRFs). In the experiment, we show that HHCRFs perform better than HHMMs (hierarchical hidden Markov models) in state sequence estimation, as the training set size becomes larger and the data source becomes non-Markovian.