Discrimination in locally stationary time series

A nearest neighbor approach to the classification of non-stationary time series is considered. A metric or measure of dissimilarity is computed between a new-to-be classified time series and each of a set of labeled sample time series. The new time series is classified by nearest neighbor rules. The metric is related to the criterion functional used in prediction error time series modeling methods. Engine fault time series data is considered. That data appears to be locally stationary. A Householder transformation - Akaike AIC criterion method for modeling time series by locally stationary AR models is applied to classify the data.