On Improving the Product Price Prediction Methods Using Deep Learning Approaches
Professor KENLI LI
Computer Science and Technology
College of Computer Science and Electronic Engineering
Due to today’s transition to online shopping in the last few decades, predicting product prices of e-commerce is gaining importance. Likewise, commodity prices are a significant factor for mining projects, as price volatility is an important parameter for mining company evaluation and investment decision making. In this work, we propose four contributions (i.e., prediction models) to address four price prediction problems using statistical, machine learning and deep learning methods. We utilized several algorithms from each domain.
The first two contributions address the price prediction problem of e-commerce products to fill in the gap of the current price prediction models for addressing second-hand items of e-commerce products.
The third and fourth contributions are introduced for addressing price forecasting of two vital energy markets, i.e., coal and oil markets using hybrid deep learning and ensemble learning methods, respectively.
In summary, the results demonstrate the superiority of the proposed methods over the baseline models. All the proposed methods lead to performance improvement.
Ahmed Fathalla, Ahmad Salah, Kenli Li, Keqin Li and Piccialli Francesco, Deep end-to-end learning for price prediction of second-hand items, Knowledge and Information Systems, 2020. SCI IF:2.936 (Q1).
Zakaria Alameer, Ahmed Fathalla, Kenli Li, Haiwang Ye and Zhang Jianhua, Multistep-ahead forecasting of coal prices using a hybrid deep learning model, Resources Policy, 2020. SSCI IF:3.986 (Q1).
Ahmed Fathalla, Ken Li, Ahmad Salah and Marwa F Mohamed, An LSTM-based Distributed Scheme for Data Transmission Reduction of IoT Systems, Neurocomputing, 2021. SCI IF:4.438 (Q1).
版权所有©湖南大学2017 湖南大学党委宣传部 地址：湖南省长沙市岳麓区麓山南路麓山门 邮编：410082 Email：email@example.com 域名备案信息：[www.hnu.edu.cn,www.hnu.cn/湘ICP备05000239号] [hnu.cn 湘教QS3-200503-000481 hnu.edu.cn 湘教QS4-201312-010059]