Satış tahmini için uzun kısa-süreli bellek ağı tabanlı derin transfer öğrenme yaklaşımı

Üretim ve hizmet sektörlerinde faaliyet gösteren firmalar, artan rekabet koşulları ile mücadele edebilmek için belirsizlik altında geleceğe yönelik çeşitli kararlar alırlar. Bu kritik kararlardan biri satış tahminidir. Dijital teknolojilerin yaygınlaşması ile derin öğrenme yaklaşımlarının satış tahmininde kullanımı artmaktadır. Derin öğrenme, başarılı sonuçlar vermesine rağmen büyük miktarda veri ile uzun eğitim sürelerine ihtiyaç duymaktadır. Bu duruma çözüm olarak problemler arası bilgi aktarımını sağlayan transfer öğrenme (TL) kullanılmaktadır. Transfer öğrenme, kaynak veriler ile modelin eğitimini ve hedef veriye aktarımını sağlamaktadır. Bu çalışmada, farklı ürünlerin satış tahmini modellerinden elde edilen bilginin gelecekteki tahmin modellerine aktarımını sağlamak üzere derin transfer öğrenme yaklaşımı önerilmiştir. Satış verisi tek değişkenli zaman serisi olarak ele alınmıştır. Kaynak veri seçiminde aktarılabilirlik ölçütü olarak hedef ve kaynak veri arasındaki gerçek cezalı düzenleme uzaklığı (ERP) kullanılmıştır. Seçilen kaynak veri ile zamansal bağımlılıkların modellenmesini sağlayan uzun kısa vadeli hafıza (LSTM) ağı eğitilmiştir. Ön eğitilen LSTM ağında parametre transferi yapılarak hedef veri için ERP-LSTM-TL tahmin modeli oluşturulmuştur. Çeşitli sektörlere ait satış veri kümelerinde yapılan deneysel çalışmalarda ERP-LSTM-TL, hedef veri ile eğitilen LSTM’e göre tahmin doğruluğunda ve eğitim süresinde iyileşme sağlamıştır. Önerilen yaklaşımın performansı klasik tahmin ve makine öğrenmesi yöntemlerinin performansları ile karşılaştırılmıştır. ERP-LSTM-TL karşılaştırılan yöntemlere göre istatistiksel olarak daha iyi sonuç vermiştir.

Anahtar Kelimeler:

Uzun kısa vadeli hafıza (LSTM), satış tahmini, transfer öğrenme, kaynak seçimi, gerçek cezalı düzenleme uzaklığı

Long short-term memory network based deep transfer learning approach for sales forecasting

Firms that operate in the production and service sectors take various decisions for the future under uncertainty in order to combat the increasing competitive conditions. One of these critical decisions is sales forecasting. With the spread of digital technologies, the use of deep learning approaches in sales forecasting is increasing. Although deep learning gives successful results, it needs long training time with large amounts of data. As a solution to this situation, transfer learning (TL), which provides information transfer between problems, is used. Transfer learning provides the training of the source data and its transfer to the target data. In this study, a deep transfer learning approach is proposed to transfer the information obtained from the sales forecasting models of different products to future forecasting models. Sales data are considered as univariate time series. The edit distance with real penalty (ERP) between the target and source data is used as a measure of transferability in the selection of source data. A long short-term memory (LSTM) network has been trained, which enables modeling of temporal dependencies with the selected source data. ERP-LSTM-TL forecasting model is created for the target data by transferring parameters from the pre-trained LSTM network. In the experimental studies with sales datasets from various industries, ERP-LSTM-TL improved the forecasting accuracy and training time compared to the LSTM trained with target data. The performance of the proposed approach was compared with the performances of the classical forecasting and machine learning methods. ERP-LSTM-TL yielded statistically better results than the compared methods.

Keywords:

Long short-term memory (LSTM), sales forecasting, transfer learning, source selection, edit distance with real penalty,

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