Comparison of Deep Learning Methods for Sales Forecasting

Year 2024, Volume: 29 Issue: 2, 535 - 554, 30.08.2024

Begüm Erol , Tülin İnkaya

https://doi.org/10.17482/uumfd.1382971

Abstract

With the digital transformation, the importance of big data analytics in the supply chain management has been increasing day by day. Especially, the use of big data in fast and accurate estimation of customer demand provides companies competitive advantage. In this direction deep learning models, which is one of the artificial intelligence techniques, stand out in the discovery of complex patterns in big data. In the recent years, several deep learning methods have been proposed in the literature. In this study, the performances of deep learning methods for the sales forecasting problem are compared. In this context, deep neural network (DNN), deep autoencoder (DAE), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) network, bidirectional LSTM (Bi-LSTM) network, gated recurrent unit (GRU), CNN-LSTM and convolutional LSTM (ConvLSTM) methods have been applied. Experimental studies were carried out using sales data from various sectors. After hyperparameter optimization, the performances of the methods discussed were compared in terms of forecasting accuracy and training time, and the statistical significance of the results was evaluated. As a result, it has been seen that LSTM and GRU models gave successful results in the prediction accuracy, and CNN model shortens the training time.

Keywords

Deep Learning, Sales Forecasting, Time Series, Convolutional Neural Network, Recurrent Neural Network, Long Short-Term Memory Network

Project Number

FDK2021-518

SATIŞ TAHMİNİ İÇİN DERİN ÖĞRENME YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Abstract

Dijital dönüşüm ile tedarik zinciri yönetiminde büyük veri analitiğinin önemi gün geçtikçe artmaktadır. Özellikle müşteri taleplerinin hızlı ve doğru tahmin edilmesinde büyük verinin kullanımı firmalara rekabet avantajı sağlamaktadır. Bu doğrultuda, yapay zekâ tekniklerinden biri olan derin öğrenme modelleri büyük verideki karmaşık örüntülerin keşfedilmesinde öne çıkmaktadır. Son yıllarda literatürde çok sayıda derin öğrenme yöntemi önerilmiştir. Bu çalışmada, satış tahmini problemi için derin öğrenme yöntemlerinin performansları karşılaştırılmıştır. Bu kapsamda derin sinir ağı (DNN), derin otokodlayıcı (Deep AE), evrişimli sinir ağı (CNN), tekrarlayan sinir ağı (RNN), uzun kısa-süreli bellek (LSTM) ağı, çift yönlü LSTM (Bi-LSTM) ağı, kapılı tekrarlayan birim (GRU), CNN-LSTM ve evrişimli LSTM (ConvLSTM) yöntemleri uygulanmıştır. Çeşitli sektörlere ait satış verileri kullanılarak deneysel çalışmalar gerçekleştirilmiştir. Hiperparametre optimizasyonu ardından ele alınan yöntemlerin performansları tahmin doğruluğu ve eğitim süreleri açısından karşılaştırılarak sonuçların istatistiksel anlamlılığı değerlendirilmiştir. Sonuç olarak, LSTM ve GRU modellerinin tahmin doğruluğunda başarılı sonuçlar verdiği, CNN modelinin ise eğitim süresini kısalttığı görülmüştür.

Keywords

Derin Öğrenme, Satış Tahmini, Zaman Serisi, Evrişimli Sinir Ağı, Tekrarlayan Sinir Ağı, Uzun Kısa-Süreli Bellek Ağı

Ethical Statement

Etik beyana gerek bulunmamaktadır.

Supporting Institution

Bursa Uludağ Üniversitesi (BUÜ) Bilimsel Araştırma Projeleri (BAP) Birimi

Project Number

FDK2021-518

Thanks

Bu araştırma Bursa Uludağ Üniversitesi (BUÜ) Bilimsel Araştırma Projeleri (BAP) Birimi tarafından desteklenmiştir (Proje Kodu: FDK2021-518).

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