For a given time series observation sequence, we can estimate the
parameters of the AutoRegression Moving Average (ARMA) model, thereby
representing a potentially long time series by a limited dimensional
vector. In many applications, these parameter vectors will be
separable into different groups, due to the different underlying
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. In this paper, we first give a brief
introduction to the theory of time series. We then discuss in detail
our approach to time series recognition, using the ARMA model, and
finish with experimental results.
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