Summary of Carbon Price Fluctuation Prediction Using Blockchain Information a New Hybrid Machine Learning Approach, by H. Wang et al.
Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach
by H. Wang, Y. Pang, D. Shang
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel hybrid machine learning approach proposed in this study is designed to predict carbon price fluctuations. The framework combines DILATED Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) neural networks, allowing for efficient feature extraction. A regularization method using L1 and L2 parameter norm penalties is applied to make predictions. By incorporating blockchain-related indicators through the regularization process, the model leverages high correlations between energy indicator prices and blockchain information found in previous literature. Experimental results show that the DILATED CNN-LSTM framework outperforms traditional architectures, while Ridge Regression (RR) as L2 regularization proves better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization for price forecasting. The proposed RR-DILATED CNN-LSTM approach effectively and accurately predicts carbon price fluctuations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops a new way to predict changes in the price of carbon, an important indicator of environmental health. It combines two types of machine learning models: DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. By using information from blockchain technology, which tracks transactions on the internet, this approach can make more accurate predictions than previous methods. The results show that this new method is better at forecasting price changes than older techniques. |
Keywords
» Artificial intelligence » Cnn » Feature extraction » Lstm » Machine learning » Regression » Regularization
