Open Access

ARTICLE

Price Prediction of Seasonal Items Using Time Series Analysis

Ahmed Salah^1,2, Mahmoud Bekhit³, Esraa Eldesouky^4,5, Ahmed Ali^4,6,*, Ahmed Fathalla⁷

1 Faculty of Computers and Informatics, Zagazig University, Sharkeya, 44523, Egypt
2 Information Technology Department, the University of Technology and Applied Sciences, Ibri, Oman
3 School of Electrical and Data Engineering, University of Technology Sydney, Sydney, 2007, Australia
4 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
5 Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522, Egypt
6 Higher Future Institute for Specialized Technological Studies, Cairo, 3044, Egypt
7 Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, 41522, Egypt

* Corresponding Author: Ahmed Ali. Email:

Computer Systems Science and Engineering 2023, 46(1), 445-460. https://doi.org/10.32604/csse.2023.035254

Received 14 August 2022; Accepted 28 October 2022; Issue published 20 January 2023

Abstract

The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices as a time series task. We proposed utilizing two different approaches to address this problem, namely, 1) machine learning (ML)-based models and 2) deep learning (DL)-based models. Thus, this research tuned a set of well-known predictive models on a real-life dataset. Those models are ensemble learning-based models, random forest, Ridge, Lasso, and Linear regression. Moreover, two new DL architectures based on gated recurrent unit (GRU) and long short-term memory (LSTM) models are proposed. Then, the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics, where the evaluation includes both numerical and visual comparisons of the examined models. The obtained results show that the ensemble learning models outperformed the classic machine learning-based models (e.g., linear regression and random forest) and the DL-based models.

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Keywords

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