Forecasting the Turkish Cinema Market (2025-2026): A Machine Learning Approach to Post-Pandemic Recovery
Abstract
This study focuses on a predictive statistical analysis of cinema attendance in Türkiye, utilising annual data from
2000 to 2024. The primary methodological challenge in time-series forecasting for this sector is the non-structural
shock caused by the COVID-19 pandemic (2020-2021). This study employs a machine learning approach that
treats these years as outliers. By interpolating the trend across the pandemic gap, we generate an “Aggressive
Recovery” scenario using Holt-Winters Exponential Smoothing. Even under the -5% sensitivity constraint, total
admissions are projected to remain firmly within the bounds of a robust, post-pandemic recovery.
Keywords
Cinema attendance, machine learning, Holt-Winters exponential smoothing
Supporting Institution
Anadolu University
Ethical Statement
None
References
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