Classificação de séries temporais via Classificador de Bayes empregando Modelos Lineares Dinâmicos

Abstract

In this work we present a new approach for applications in Discriminant Analysis (DA) to problems whose observations in the training set are from time series, using the Bayes classifier and modeling the classes distributions in with Linear Dynamic Models. Theoretical developments were conducted to obtain an analytic form for the classe posterior probability. The simulation studies have been developed to evaluate the proposed approach, to evaluate different strategies to estimate the model variance and determine the classification error rates (ET) to compare them with other usual approaches in AD. Time series were simulated with different structures of classes separation and with different sizes for the training set. The proposed approach was also applied to data from real problems with different degrees of difficulty with respect to the classes number, the time series size and number of observations in the training set. With real data the proposed classifier was compared with other classifiers in terms of error rate. Although it is needed most complete studies, the results suggest that this parametric approach developed constitutes a promising alternative for problems in AD with time series, particularly in a challenging context when the size time series is much large than the number of observations in the classes.