Zaman Serisi Tabanlı Talep Tahmini: Holt-Winters, SARIMA ve Prophet Modellerinin Perakende Envanter Verisi Üzerindeki Karşılaştırması

Yıl 2025, Cilt: 24 Sayı: 53, 641 - 668, 29.09.2025

Alican Doğan

https://doi.org/10.46928/iticusbe.1725584

Öz

Perakende sektöründe stok seviyelerinin doğru bir şekilde yönetilmesi, müşteri memnuniyetinin artırılması ve lojistik maliyetlerin azaltılması açısından büyük önem taşımaktadır. Bu doğrultuda, talep tahmini modellerinin doğruluğu, işletmelerin karar destek sistemlerinde kritik bir rol oynamaktadır. Bu çalışmada, perakende mağazalarında ürün talebini zaman serisi analiz yöntemleriyle tahmin etmek amacıyla klasik modellere dayalı bir karşılaştırma yapılmıştır. Analizlerde, 73.000’den fazla günlük satış kaydı içeren Retail Store Inventory Forecasting Dataset adlı sentetik ancak gerçekçi bir veri seti kullanılmıştır. Çalışma kapsamında üç farklı zaman serisi modeline odaklanılmıştır: Holt-Winters Üstel Düzeltme Modeli, Mevsimsel ARIMA (SARIMA) ve Prophet. Bu modeller, günlük ürün satışlarını tahmin etmek üzere uygulanmış ve performansları Ortalama Mutlak Hata (MAE), Kök Ortalama Kare Hata (RMSE) ve Ortalama Mutlak Yüzde Hata (MAPE) metrikleri ile test verisi üzerinde değerlendirilmiştir. Model çıktıları karşılaştırıldığında, Holt-Winters modeli, en düşük hata oranlarını vererek en başarılı tahmin performansını göstermiştir. Elde edilen bulgular, özellikle mevsimsellik ve trend gibi yapısal bileşenlerin öne çıktığı perakende satış verilerinde, klasik zaman serisi modellerinin hala güçlü ve etkili araçlar olduğunu ortaya koymaktadır. Bu çalışma, işletmelere daha doğru talep tahminleri ile stok fazlası ve stok yetersizliği gibi sorunları azaltma fırsatı sunmakta ve zaman serisi tabanlı yaklaşımların karar destek sistemlerinde nasıl kullanılabileceğine dair değerli bir çerçeve önermektedir.

Anahtar Kelimeler

Zaman serisi analizi , Stok yönetimi , Talep tahmini

Time Series-Based Demand Forecasting: A Comparative Analysis of Holt-Winters, SARIMA, and Prophet Models on Retail Inventory Data

Öz

Accurate inventory management is a critical component of operational efficiency in the retail sector, directly influencing customer satisfaction and logistics cost optimization. Demand forecasting plays a pivotal role in this process by enabling businesses to anticipate future product needs and make data-driven decisions. This study aims to evaluate and compare the performance of classical time series models for demand forecasting using a rich and structured dataset. The analysis is based on the Retail Store Inventory Forecasting Dataset, a synthetic but realistic collection comprising over 73,000 daily sales records across multiple products and retail locations. Three prominent time series forecasting methods were selected for this research: the Holt-Winters Exponential Smoothing Model, the Seasonal ARIMA (SARIMA) model, and Prophet. These models were implemented to predict daily product demand and evaluated on a test set using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) as performance metrics. Among the three, the Holt-Winters model delivered the best forecasting performance with the lowest error values. The findings reveal that classical time series models remain powerful tools for retail forecasting tasks, especially when capturing patterns driven by seasonality and trend. Furthermore, this study demonstrates that robust forecasting techniques can substantially support inventory optimization efforts, helping to mitigate common challenges such as overstocking and stockouts. By highlighting the practical value of interpretable and well-established models, this research provides a foundational perspective for integrating time series forecasting into business intelligence and decision support systems.

Anahtar Kelimeler

Time series analysis , Inventory management , Demand forecasting

Kaynakça

• Barati, S. (2025). A system dynamics approach for leveraging blockchain technology to enhance demand forecasting in supply chain management. Supply Chain Analytics, 10, Article 100115. https://doi.org/10.1016/j.sca.2025.100115
• Coppola, P., De Fabiis, F., & Silvestri, F. (2025). Urban Air Mobility demand forecasting: Modeling evidence from the case study of Milan (Italy). European Transport Research Review, 17(1), Article 2. https://doi.org/10.1186/s12544-024-00700-x
• Guo, S., Ni, H., Xu, D., Li, C., Luo, Z., & Tan, W. (2025). Energy demand forecasting using ridgelet neural networks boosted Beluga whale optimization. Engineering Research Express, 7(2), Article 025331. https://doi.org/10.1088/2631-8695/adcdc9
• Hu, M., Liang, W., Qiu, R. T. R., & Wu, D. C. (2025). Tourism demand forecasting using compound pattern recognition. Tourism Management, 109, Article 105138. https://doi.org/10.1016/j.tourman.2025.105138
• Kampp, M., Sedelmeier, J., Schüth, J., Thust, M., Kaiser, D., Scherr, W., Schlaich, J., & Senk, P. (2025). Design of a European high-speed rail network and use of passenger demand forecasting to test European policy targets. European Transport Research Review, 17(1), Article 23. https://doi.org/10.1186/s12544-025-00715-y
• Khan, S., Mazhar, T., Shahzad, T., Ali, T., Ayaz, M., Ghadi, Y. Y., Aggoune, E.-H. M., & Hamam, H. (2025). Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs): A seasonal approach. Scientific Reports, 15(1), Article 19349. https://doi.org/10.1038/s41598-025-04301-z
• Lee, H., Kameda, K., Manzhos, S., & Ihara, M. (2025). A novel encoding method for high-dimensional categorical data for electricity demand forecasting in distributed energy systems. Applied Energy, 392, Article 125989. https://doi.org/10.1016/j.apenergy.2025.125989
• Li, G., Yang, Y., Liu, Z., He, Z., & Li, C. (2025). Electricity demand forecasting and power supply planning under carbon neutral targets. Energy Reports, 13, 2740–2751. https://doi.org/10.1016/j.egyr.2025.02.015
• Liu, M., Zhao, W., Zhou, Y., & Eslami, M. (2025). Load demand forecasting in air conditioning: A rotor Hopfield neural network approach optimized by a new optimization algorithm. Scientific Reports, 15(1), Article 18774. https://doi.org/10.1038/s41598-025-02568-w
• Matkovic, D., Pilski, T. M., & Capuder, T. (2025). Participation of electric vehicle charging station aggregators in the day-ahead energy market using demand forecasting and uncertainty-based pricing. Energy, 328, Article 136299. https://doi.org/10.1016/j.energy.2025.136299
• Mikulić, J., & Baumgärtner, R. M. (2025). Google Trends and Baidu index data in tourism demand forecasting: A critical assessment of recent applications. Tourism Management, 110, Article 105164. https://doi.org/10.1016/j.tourman.2025.105164
• Ning, Z., Jin, M., & Zeng, P. (2025). A multimodal interaction-driven feature discovery framework for power demand forecasting. Energies, 18(11), Article 2907. https://doi.org/10.3390/en18112907
• Nygård, H. S., Grøtan, S., Kvisberg, K. R., Gorjão, L. R., & Martinsen, T. (2025). Enhancing peak electricity demand forecasting for commercial buildings using novel LSTM loss functions. Electric Power Systems Research, 246, Article 111722. https://doi.org/10.1016/j.epsr.2025.111722
• Radfar, S., Koosha, H., Gholami, A., & Amindoust, A. (2025). A neuro-fuzzy and deep learning framework for accurate public transport demand forecasting: Leveraging spatial and temporal factors. Journal of Transport Geography, 126, Article 104217. https://doi.org/10.1016/j.jtrangeo.2025.104217
• Seyam, A., Mathew, S. S., Du, B., Barachi, M. E., & Shen, J. (2025). A stacking ensemble model for food demand forecasting: A preventative approach to food waste reduction. Cleaner Logistics and Supply Chain, 15, Article 100225. https://doi.org/10.1016/j.clscn.2025.100225
• Subramanian, B., Mishra, A., Ramkumar, B. V., Mandala, G., Kathamuthu, N. D., & Srithar, S. (2025). Big data and fuzzy logic for demand forecasting in supply chain management: A data-driven approach. Journal of Fuzzy Extension and Applications, 6(2), 260–283. https://doi.org/10.22105/jfea.2025.488816.1703
• Wu, Z., Kan, X., Chen, Z., Fang, Q., Wei, Z., & Fang, J. (2025). Application of time series-based demand forecasting in inventory management for energy enterprises. Association for Computing Machinery. https://doi.org/10.1145/3724154.3724362
• Zhang, Y., Chen, Z., Feng, B., Sui, X., & Zhang, S. (2025). Granger-guided reduced dual attention long short-term memory for travel demand forecasting during coronavirus disease 2019. Engineering Applications of Artificial Intelligence, 153, Article 110950. https://doi.org/10.1016/j.engappai.2025.110950
• Zhou, Z., Zhou, H., Qiao, Y., Wang, S., & Li, M. (2025). An optimization protocol for MRI examination resource allocation based on demand forecasting and linear programming. Scientific Reports, 15, Article 15076. https://doi.org/10.1038/s41598-025-98817-z