Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach
- URL: http://arxiv.org/abs/2411.02709v1
- Date: Tue, 05 Nov 2024 01:12:17 GMT
- Title: Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach
- Authors: H. Wang, Y. Pang, D. Shang,
- Abstract summary: This study integrates DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm.
Since 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.
- Score: 2.0387986050706326
- License:
- 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.
Related papers
- Stock Price Prediction using Dynamic Neural Networks [0.0]
This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices.
Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data.
arXiv Detail & Related papers (2023-06-18T20:06:44Z) - Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning
Methods [0.0]
This paper provides an update on work previous to 2019 on the link between EthUSD BitUSD and gas price.
For forecasting, we compare a novel combination of machine learning methods such as Direct Recursive Hybrid LSTM, CNNLSTM, and Attention LSTM.
arXiv Detail & Related papers (2023-05-14T08:51:44Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines.
We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling
and Design [68.1682448368636]
We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks.
To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings.
We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
arXiv Detail & Related papers (2022-03-29T21:18:47Z) - Neural Capacitance: A New Perspective of Neural Network Selection via
Edge Dynamics [85.31710759801705]
Current practice requires expensive computational costs in model training for performance prediction.
We propose a novel framework for neural network selection by analyzing the governing dynamics over synaptic connections (edges) during training.
Our framework is built on the fact that back-propagation during neural network training is equivalent to the dynamical evolution of synaptic connections.
arXiv Detail & Related papers (2022-01-11T20:53:15Z) - Wholesale Electricity Price Forecasting using Integrated Long-term
Recurrent Convolutional Network Model [0.0]
This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices.
Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting.
arXiv Detail & Related papers (2021-12-23T06:45:12Z) - Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error
(ERMSE): Deep Learning for Stock Price Movement Prediction [15.165487282631535]
This paper aims to improve prediction accuracy and minimize forecasting error loss by using Generative Adversarial Networks.
It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM.
arXiv Detail & Related papers (2021-11-30T18:38:59Z) - Towards an Understanding of Benign Overfitting in Neural Networks [104.2956323934544]
Modern machine learning models often employ a huge number of parameters and are typically optimized to have zero training loss.
We examine how these benign overfitting phenomena occur in a two-layer neural network setting.
We show that it is possible for the two-layer ReLU network interpolator to achieve a near minimax-optimal learning rate.
arXiv Detail & Related papers (2021-06-06T19:08:53Z) - Neural Networks Enhancement with Logical Knowledge [83.9217787335878]
We propose an extension of KENN for relational data.
The results show that KENN is capable of increasing the performances of the underlying neural network even in the presence relational data.
arXiv Detail & Related papers (2020-09-13T21:12:20Z) - A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock
Prices and News [7.578363431637128]
This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources to predict future stock movement.
The blending ensemble model contains two levels. The first level contains two Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and one Gated Recurrent Units network (GRU)
The fully connected neural network is used to ensemble several individual prediction results to further improve the prediction accuracy.
arXiv Detail & Related papers (2020-07-23T15:25:37Z) - Continual Learning in Recurrent Neural Networks [67.05499844830231]
We evaluate the effectiveness of continual learning methods for processing sequential data with recurrent neural networks (RNNs)
We shed light on the particularities that arise when applying weight-importance methods, such as elastic weight consolidation, to RNNs.
We show that the performance of weight-importance methods is not directly affected by the length of the processed sequences, but rather by high working memory requirements.
arXiv Detail & Related papers (2020-06-22T10:05:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.