E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini

Year 2023, Volume: 7 Issue: 1, 59 - 70, 02.01.2024

Alp Ecevit İrem Öztürk Mustafa Dağ Tuncay Özcan

https://doi.org/10.26650/acin.1259067

Abstract

Satış tahmininin doğruluğu, e-ticaret işletmelerinin envanter yönetimini, fiyatlandırma kararlarını, pazarlama stratejilerini ve personel planlamasını en iyilemesi için çok önemlidir. Bu noktada, satış tahmini için istatistiksel modeller, bulanık sistemler, makine öğrenmesi ve derin öğrenme algoritmaları gibi farklı yaklaşımlar yaygın olarak kullanılmaktadır. Bu çalışma, derin öğrenme tabanlı Uzun-Kısa Süreli Bellek (LSTM) modeli ve Facebook Prophet modelinin kısa vadeli satış tahmini üzerindeki performansını incelemektedir. Önerilen modellerin performansı, bir e-ticaret sitesinden alınan gerçek hayat verileri kullanılarak mevsimsel otoregresif bütünleşik hareketli ortalama (SARIMA) ile karşılaştırılmıştır. Önerilen tahmin modellerinin karşılaştırmalı analizi için, performans ölçütleri olarak ağırlıklı ortalama mutlak yüzde hata (wMAPE), hata kareleri ortalamasının karekökü (RMSE) ve R-kare seçilmiştir. Sayısal sonuçlar, LSTM modelinin saatlik satış tahmini için tahmin doğruluğu açısından Prophet ve SARIMA modellerinden daha iyi performans gösterdiğini göstermiştir.

Keywords

Satış tahmini, E-ticaret, LSTM, Prophet

Short-Term Sales Forecasting Using LSTM and Prophet Based Models in E-Commerce

Abstract

The accuracy of sales forecasting is crucial for e-commerce businesses to optimize inventory management, pricing decisions, marketing strategies and staff scheduling. At this point, different approaches such as statistical models, fuzzy systems, machine learning and deep learning algorithms are widely used for sales forecasting. This study investigates the performance of the deep learning based the Long-Short Term Memory (LSTM) model and the Facebook Prophet model on short-term sales forecasting. The performance of the proposed models is compared with the seasonal autoregressive integrated moving average (SARIMA) using real-life data from an e-commerce site. For the comparative analysis of the proposed forecasting models, weighted average absolute percent error (wMAPE), root mean square error (RMSE) and R-squared are selected as performance measures. The numerical results show that the LSTM model outperforms the Prophet and SARIMA models in terms of forecast accuracy for hourly sales forecasting.

Keywords

Sales Forecasting, E-commerce, LSTM, Prophet

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