A DeepFM-Based Non-Parametric Model Enabled Big Data Platform for Predicting Passenger Car Sales in Sustainable Way-Reference-Cited by-同舟云学术

A DeepFM-Based Non-Parametric Model Enabled Big Data Platform for Predicting Passenger Car Sales in Sustainable Way

Published:2023-12 Issue:12 Volume:24 Page:16018-16028
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Liu Weixing¹,Zhang Zunqian²,Bai Yunjie³,Liu Yikai³,Yang Aimin³^ORCID,Li Jie¹^ORCID

Affiliation:

1. Key Laboratory of Engineering Computing in Tangshan City, the Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence, and the Comprehensive Testing and Analyzing Center, North China University of Science and Technology, Tangshan, China

2. Key Laboratory of Engineering Computing in Tangshan City, the Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence, and the College of Metallurgy and Energy, North China University of Science and Technology, Tangshan, China

3. Hebei Key Laboratory of Data Science and Application, the Key Laboratory of Engineering Computing in Tangshan City, the Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence, and the College of Science, North China University of Science and Technology, Tangshan, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10339106/10220433.pdf?arnumber=10220433

Reference29 articles.

1. Analysis of Dimensionality Reduction Techniques on Big Data

2. The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices

3. Predicting Vehicle Sales by Sentiment Analysis of Twitter Data and Stock Market Values

4. ForeXGBoost: passenger car sales prediction based on XGBoost

5. Recommender systems for product bundling

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