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

Begüm Erol; Tülin İnkaya

Makale detayları

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

Yazarlar:

Begüm Erol | Tülin İnkaya

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 1, pp.191-202

Belge Tipi:

Makale

Yayın Tarihi:

8/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289401

pISSN:

1300-1884

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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 LSTMe 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.

Makale detayları

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

Yazarlar:

Begüm Erol | Tülin İnkaya

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 1, pp.191-202

Belge Tipi:

Makale

Yayın Tarihi:

8/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289401

pISSN:

1300-1884

Long short-term memory (LSTM)

sales forecasting

transfer learning

source selection

edit distance with real penaltyUzun kısa vadeli hafıza (LSTM)

satış tahmini

transfer öğrenme

kaynak seçimi

gerçek cezalı düzenleme uzaklığı

Sayı

Makale detayları

Yazarlar: Begüm Erol | Tülin İnkaya

Yayıncı: Gazi University

Ana Başlıklar: Mühendislik

Dergi: Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 1, pp.191-202

Belge Tipi: Makale

Yayın Tarihi: 8/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001289401

pISSN: 1300-1884

Sayı

Yazarlar:

Begüm Erol | Tülin İnkaya

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 1, pp.191-202

Belge Tipi:

Makale

Yayın Tarihi:

8/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289401

pISSN:

1300-1884