Nearest-neighbor-based approach to time-series classification

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• 作者：Yen-Hsien Lee^a ; ^{yhlee@mail.ncyu.edu.tw}  ; [Author Vitae] ; Chih-Ping Wei^b  ; [Author Vitae] ; Tsang-Hsiang Cheng^c  ; [Author Vitae] ; Ching-Ting Yang^d  ; [Author Vitae]
• 关键词：Data mining ; Classification analysis ; Time-series classification ; k Nearest neighbor classification ; Time-series similarity ; Telecommunications data mining ; Churn prediction
• 刊名：Decision Support Systems
• 出版年：2012
• 期刊代码：5_01679236
• 类别：dec
• 出版时间：April, 2012
• 卷：53
• 期：1
• 页码：207-217
• 文件大小：1278 K

摘要

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.