Nearest-neighbor-based approach to time-series classification

Authors:
Yen-Hsien Lee;Chih-Ping Wei;Tsang-Hsiang Cheng;Ching-Ting Yang
Affiliations:
Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan, ROC;Department of Information Management, College of Management, National Taiwan University, Taipei, Taiwan, ROC;Department of Business Administration, Southern Taiwan University, Tainan, Taiwan, ROC;National Credit Card Center of R.O.C., Taipei, Taiwan, ROC
Venue:
Decision Support Systems
Year:
2012

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Abstract

Many interesting applications involve predictions based on a time-series sequence or a set of time-series sequences, which are referred to as time-series classification problems. Prior classification analysis research predominately focuses on constructing a classification model from training instances that involve non-time-series attributes. Direct application of traditional classification analysis techniques to time-series classification problems requires the transformation of time-series attributes into non-time-series ones by applying some statistical operations (e.g., average, sum, variance). However, such statistical-transformation-based approach often results in information loss and, in turn, imperils classification effectiveness. In this study, we propose a time-series classification technique based on the k-nearest-neighbor (kNN) classification approach. Using churn prediction of the mobile telecommunications industry as an evaluation application, our empirical evaluation results show that the proposed kNN-based time-series classification (kNN-TSC) technique achieves better performance (measured by miss and false alarm rates) than the statistical-transformation-based approach does.