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

Ecevit Alp; Öztürk İrem; Dağ Mustafa; Özcan Tuncay

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

Araştırma Makalesi

DOI :10.26650/acin.1259067 IUP :10.26650/acin.1259067 Tam Metin (PDF)

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

Alp Ecevit, İrem Öztürk, Mustafa Dağ, Tuncay Özcan

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.

Anahtar Kelimeler: Satış tahmini, E-ticaret, LSTM, Prophet

DOI :10.26650/acin.1259067 IUP :10.26650/acin.1259067 Tam Metin (PDF)

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

Alp Ecevit, İrem Öztürk, Mustafa Dağ, Tuncay Özcan

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.

Anahtar Kelimeler: Sales Forecasting, E-commerce, LSTM, Prophet

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DIŞA AKTAR

APA

Ecevit, A., Öztürk, İ., Dağ, M., & Özcan, T. (2023). E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini. Acta Infologica, 7(1), 59-70. https://doi.org/10.26650/acin.1259067

AMA

Ecevit A, Öztürk İ, Dağ M, Özcan T. E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini. Acta Infologica. 2023;7(1):59-70. https://doi.org/10.26650/acin.1259067

ABNT

Ecevit, A.; Öztürk, İ.; Dağ, M.; Özcan, T. E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini. Acta Infologica, [Publisher Location], v. 7, n. 1, p. 59-70, 2023.

Chicago: Author-Date Style

Ecevit, Alp, and İrem Öztürk and Mustafa Dağ and Tuncay Özcan. 2023. “E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini.” Acta Infologica 7, no. 1: 59-70. https://doi.org/10.26650/acin.1259067

Chicago: Humanities Style

Ecevit, Alp, and İrem Öztürk and Mustafa Dağ and Tuncay Özcan. “E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini.” Acta Infologica 7, no. 1 (May. 2024): 59-70. https://doi.org/10.26650/acin.1259067

Harvard: Australian Style

Ecevit, A & Öztürk, İ & Dağ, M & Özcan, T 2023, 'E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini', Acta Infologica, vol. 7, no. 1, pp. 59-70, viewed 18 May. 2024, https://doi.org/10.26650/acin.1259067

Harvard: Author-Date Style

Ecevit, A. and Öztürk, İ. and Dağ, M. and Özcan, T. (2023) ‘E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini’, Acta Infologica, 7(1), pp. 59-70. https://doi.org/10.26650/acin.1259067 (18 May. 2024).

MLA

Ecevit, Alp, and İrem Öztürk and Mustafa Dağ and Tuncay Özcan. “E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini.” Acta Infologica, vol. 7, no. 1, 2023, pp. 59-70. [Database Container], https://doi.org/10.26650/acin.1259067

Vancouver

Ecevit A, Öztürk İ, Dağ M, Özcan T. E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini. Acta Infologica [Internet]. 18 May. 2024 [cited 18 May. 2024];7(1):59-70. Available from: https://doi.org/10.26650/acin.1259067 doi: 10.26650/acin.1259067

ISNAD

Ecevit, Alp - Öztürk, İrem - Dağ, Mustafa - Özcan, Tuncay. “E-Ticarette LSTM ve Prophet Esaslı Modeller Kullanarak Kısa Dönemli Satış Tahmini”. Acta Infologica 7/1 (May. 2024): 59-70. https://doi.org/10.26650/acin.1259067

Cilt 7, Sayı 12023, S. 59-70

ZAMAN ÇİZELGESİ

Gönderim	02.03.2023
Kabul	14.03.2023
Çevrimiçi Yayınlanma	14.04.2023

LİSANS

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