Trend or no trend : a novel nonparametric method for classifying time series
Stanislav Nikolov · 2012
In supervised classification, one attempts to learn a model of how objects map to labels by selecting the best model from some model space. The choice of model space encodes assumptions about the problem. We propose a setting for model specification and selection in supervised learning based on a latent source model. In this setting, we specify the model by a small collection of unknown latent sources and posit that there is a stochastic model relating latent sources and observations. With this setting in mind, we propose a nonparametric classification method that is entirely unaware of the structure of these latent sources. Instead, our method relies on the data as a proxy for the unknown latent sources. We perform classification by computing the conditional class probabilities for an observation based on our stochastic model. This approach has an appealing and natural interpretation- that an observation belongs to a certain class if it sufficiently resembles other examples of that class. We extend this approach to the problem of online time series classification. In