A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

Name: A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China
Creator: Anlei Wei
Published: 2024-01-03T17:04:16.603Z
Keywords: Environmental Management, Environment Protection

Wei, Anlei

doi:10.17632/fsdf4f4yv5.1

A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

Published: 3 January 2024| Version 1 | DOI: 10.17632/fsdf4f4yv5.1

Contributor:

Anlei Wei

Description

This dataset includes all the necessary data and code for building prediction models, such as CNN, LSTM, CNN-LSTM, CEEMDAN, Boosting, and GRU, as required for the article.

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Institutions

Northwest University

A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

Description

Files

Institutions

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