Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales

Md. Iftekharul Alam Efat (), Petr Hajek (), Mohammad Zoynul Abedin (), Rahat Uddin Azad, Md. Al Jaber, Shuvra Aditya and M. Kabir Hassan
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Md. Iftekharul Alam Efat: Noakhali Science and Technology University
Petr Hajek: University of Pardubice
Mohammad Zoynul Abedin: Teesside University
Rahat Uddin Azad: Noakhali Science and Technology University
Md. Al Jaber: Noakhali Science and Technology University
Shuvra Aditya: Noakhali Science and Technology University

Annals of Operations Research, 2024, vol. 339, issue 1, No 12, 297-328

Abstract: Abstract Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at the store and product levels. To capture different big data characteristics in sales forecasting data, such as seasonal and trend variations, this study develops a hybrid model combining adaptive trend estimated series (ATES) with a deep neural network model. ATES is first used to model seasonal effects and incorporate holiday, weekend, and marketing effects on sales. The deep neural network model is then proposed to model residuals by capturing complex high-level spatiotemporal features from the data. The proposed hybrid model is equipped with a feature-extraction component that automatically detects the patterns and trends in time-series, which makes the forecasting model robust against noise and time-series length. To validate the proposed hybrid model, a large volume of sales data is processed with a three-dimensional data model to effectively support business decisions at the product-specific store level. To demonstrate the effectiveness of the proposed model, a comparative analysis is performed with several state-of-the-art sales forecasting methods. Here, we show that the proposed hybrid model outperforms existing models for forecasting horizons ranging from one to 12 months.

Keywords: Machine learning; Sales forecasting; Big data; Regression model; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04838-6

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