Preprocessing Time Series Data for Classification with Application to CRM

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Conference Proceeding
AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence Proceedings, 2005, pp. 133 - 142
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We develop an innovative data preprocessing algorithm for classifying customers using unbalanced time series data. This problem is directly motivated by an application whose aim is to uncover the customers churning behavior in the telecommunication industry. We model this problem as a sequential classification problem, and present an effective solution for solving the challenging problem, where the elements in the sequences are of a multi-dimensional nature, the sequences are uneven in length and classes of the data are highly unbalanced. Our solution is to integrate model based clustering and develop an innovative data preprocessing algorithm for the time series data. In this paper, we provide the theory and algorithms for the task, and empirically demonstrate that the method is effective in determining the customer class for CRM applications in the telecommunications industry. Keywords: Classification of time series data for Telecommunications Applications.
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