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.

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