Classification trees for time series
Pattern Recognition
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Classification problems with functionally structured in- put variables arise naturally in many applications. In a clinical domain, for example, input variables could include a time series of blood pressure measurements. In a financial setting, different time series of stock returns might serve as predictors. In an archaeological application, the 2-D pro- file of an artifact may serve as a key input variable. In such domains, accuracy of the classifier is not the only reason- able goal to strive for; classifiers that provide easily inter- pretable results are also of value. In this work, we present an intuitive scheme for extending decision trees to handle functional input variables. Our results show that such deci- sion trees are both accurate and readily interpretable.