12:00, Wed 26 Feb 1997, WeH 7220 Learning to Recognize Time Series: Combining ARMA models with memory-based learning Kan Deng In this talk, we propose a new method for classifying time series data. Rather than compare features, as most prior approaches do, we propose to compare the mechanisms underlying the generation of a specific observation sequence, using the AutoRegression Moving Average (ARMA) statistical model. For a given time series observation sequence, we can estimate the parameters of the ARMA model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will cluster into different groups, based on the different mechanisms that generate differing time series. We can then use classification algorithms to predict the class of a new, uncategorized time series. For the purposes of a highly autonomous system, our approach to this classification uses memory-based learning and intensive cross-validation for feature and kernel selection. In an example application, we distinguish between driving data of a skilled, sober driver vs. a drunk driver, by calculating the ARMA model for the respective time series.