Overcome Limited Data Challenge in Time Series Forecasting with Power Law Algorithm for Attribution Churn Value - Hapsari, Cindy; Yudistira, Bagus Gede Krishna (Politeknik Negeri Lhokseumawe, 2026)
Limited historical data is a major challenge in churn forecasting. This study shows that the Power Law Algorithm can model churn-related value patterns reliably even with minimal data, making it a promising approach for early churn analysis when historical data is scarce.