Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach

TL;DR

This paper introduces a novel hybrid machine learning framework combining DILATED CNN and LSTM, enhanced with regularization, to improve carbon price fluctuation prediction using blockchain information, outperforming traditional methods.

Contribution

It proposes a new RR-DILATED CNN-LSTM model that effectively integrates blockchain data and regularization for more accurate carbon price forecasting.

Findings

DILATED CNN-LSTM outperforms traditional CNN-LSTM.

Blockchain information significantly improves prediction accuracy.

L2 regularization (Ridge Regression) is more effective than L1 in this context.

Abstract

In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more efficient. Then, based on the DILATED CNN-LSTM framework, the L1 and L2 parameter norm penalty as regularization method is adopted to predict. Referring to the characteristics of high correlation between energy indicator price and blockchain information in previous literature, and we primarily includes indicators related to blockchain information through regularization process. Based on the above methods, this paper uses a dataset containing an amount of data to carry out the carbon price prediction. The experimental results show that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture. Blockchain information can effectively predict the price. Since parameter norm penalty as regularization, Ridge Regression (RR) as L2 regularization is better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization in price forecasting. Thus, the proposed RR-DILATED CNN-LSTM approach can effectively and accurately predict the fluctuation trend of the carbon price. Therefore, the new forecasting methods and theoretical ecology proposed in this study provide a new basis for trend prediction and evaluating digital assets policy represented by the carbon price for both the academia and practitioners.