Nearest‐neighbor‐based approach to time‐series classification

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.