Nearest-neighbor-based approach to time-series classification

► We highlight the importance of time-series classification problems. ► We develop the kNN-TSC technique for time-series classification problems. ► We evaluate kNN-TSC using churn prediction as the evaluation application. ► kNN-TSC generally outperforms the benchmark technique in miss and false alarm rates. ► kNN-TSC with the stratified average method is capable of handling imbalanced data sets.