Temporal classification : extending the classification paradigm to multivariate time series

Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, petal colour and petal count. The values of these attributes is assumed to be unchanging – the flower never grows or loses leaves. However, many real datasets are not “static”; they cannot sensibly be repre-sented as a fixed set of attributes. Rather, the examples are expressed as fea-tures that vary temporally, and it is the temporal variation itself that is used for classification. Consider a simple gesture recognition domain, in which the temporal features are the position of the hands, finger bends, and so on. Looking at the position of the hand at one point in time is not likely to lead to a suc-cessful classification; it is only by analysing changes in position that recognition is possible. This thesis presents a new technique for temporal classification. By extracting