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There are two paradigms for modeling varying length time series data, namely, modeling the sequence of feature vectors and modeling the sets of vectors. In this paper, we propose a regression based autoassociative model for modeling sets of vectors for time series data. We also propose a hybrid framework where a regression based autoassociative model is used for representing varying length time series data and then a discriminative model is used for classification. The proposed approach applied to speech emotion recognition task gives a better performance than the conventional methods.