Carbon Price Prediction With Public Social Media Big Data and an Interpretable Multi‐Objective Intelligent Feature Optimization Strategy

Fuente: Wiley Open Access

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Honggang Guo, Shuang Bi, Yu Jin, Houhang Zhao, Yutong Ai
Format:	Artículo Open Access
Veröffentlicht:	Wiley 2026
Schlagworte:	Journal of Forecasting
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Explainable Soybean Futures Price Forecasting Based on Multi‐Source Feature Fusion
von: Binrong Wu, et al.
Veröffentlicht: (2024)

Forecasting and Pricing in the Carbon Credits Market
von: Yiyang Chen, et al.
Veröffentlicht: (2026)

Can Attention Mechanisms Improve Carbon Price Forecasting Accuracy?
von: Ting Yao, et al.
Veröffentlicht: (2025)

A New Multivariate Decomposition–Ensemble Approach With Multisource Heterogeneous Data for Crude Oil Price Forecasting
von: Zhengling Zhao, et al.
Veröffentlicht: (2026)

Media and Business Cycle Predictability
von: Salim Baz, et al.
Veröffentlicht: (2026)

Stock Price Limit and Its Predictability in the Chinese Stock Market
von: Haohui Liang, et al.
Veröffentlicht: (2024)

Media Tone: The Role of News and Social Media on Heterogeneous Inflation Expectations
von: Joni Heikkinen, et al.
Veröffentlicht: (2024)

Data Quality Improvement for Financial Distress Prediction: Feature Selection, Data Re‐Sampling, and Their Combinations in Different Orders
von: Chih‐Fong Tsai, et al.
Veröffentlicht: (2025)

Spread Option Pricing Method Based on Nonparametric Predictive Inference Copula
von: Ting He
Veröffentlicht: (2025)

Stock Return Prediction Based on a Functional Capital Asset Pricing Model
von: Ufuk Beyaztas, et al.
Veröffentlicht: (2025)

Optimized Internet of Things‐Based Intelligent Energy Management System for Enhanced Renewable Generation through Advanced Forecasting and Load Strategies
von: X. Mercilin Raajini, et al.
Veröffentlicht: (2026)

Fire Prediction and Risk Identification With Interpretable Machine Learning
von: Shan Dai, et al.
Veröffentlicht: (2025)

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
von: Saptarshi Ganguly, et al.
Veröffentlicht: (2026)

IWSL Model: A Novel Credit Scoring Model With Interpretable Features for Consumer Credit Scenarios
von: Runchi Zhang, et al.
Veröffentlicht: (2025)

On the Optimal Selection of Time‐Lag Embedding Dimension for Deep Learning Approaches in Financial Forecasting With Big Data
von: Mohammadreza Ghadimpour, et al.
Veröffentlicht: (2025)

Correction to “Smart Forecasting of Carbon Prices Using Machine Learning and Neural Networks: When ARIMA Meets XGBoost and LSTM”
Veröffentlicht: (2026)

A Two‐Stage Interpretable Model to Explain Classifier in Credit Risk Prediction
von: Lu Wang, et al.
Veröffentlicht: (2025)

Default Prediction Framework With Optimal Feature Set and Matching Ratio
von: Guotai Chi, et al.
Veröffentlicht: (2025)

Forecasting House Prices: The Role of Market Interconnectedness
von: Zac Chen, et al.
Veröffentlicht: (2026)

Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning
von: Yong Qu, et al.
Veröffentlicht: (2024)

A Novel Interpretable Deep Learning‐Based Wind Speed and Power Generation Forecasting Using Multiscale Attention and Post Hoc Feature Importance Mechanism
von: Haoyu Fang, et al.
Veröffentlicht: (2025)

On Capturing Multi‐Scale Market Dynamics for High‐Frequency Stock Price Forecasting Using a Hybrid Attention‐Based Deep Learning Model
von: Runze Jiang, et al.
Veröffentlicht: (2026)

Information Illusion: Different Amounts of Information and Stock Price Estimates
von: Andreas Oehler, et al.
Veröffentlicht: (2025)

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
von: Talha Omer, et al.
Veröffentlicht: (2026)

Using a Wage–Price‐Setting Model to Forecast US Inflation
von: Nguyen Duc Do
Veröffentlicht: (2024)

Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques
von: Soňa Benecká
Veröffentlicht: (2026)

Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price
von: Nezir Köse, et al.
Veröffentlicht: (2025)

Affinities and Complementarities of Methods and Information Sets in the Estimation of Prices in Real Estate Markets
von: Mirko S. Bozanic‐Leal, et al.
Veröffentlicht: (2024)

Adaptive Model Integration for Stock Price Forecasting With CNN‐ATTN‐BiLSTM
von: Yi Xiao, et al.
Veröffentlicht: (2026)

Forecasting European Union Electronic Trading Systems Phase 4 Spot Prices Using Data‐Driven Hybrid Deep Learning Models: Integrating Energy and Market Activity as Controls
von: Noman Arshed, et al.
Veröffentlicht: (2026)

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches
von: Björn Schulte‐Tillmann, et al.
Veröffentlicht: (2026)

Enhancing the Potential of Random Forest Technique With an Adaptive Horse Herd Optimization Framework for Electric Load Prediction
von: Manvi Mishra, et al.
Veröffentlicht: (2026)

The Impact of News Sentiment on the Bitcoin Price via Machine Learning and Deep Learning‐Based NLP Models
von: Yunus Emre Gür, et al.
Veröffentlicht: (2025)

Interpretable corn future price forecasting with multivariate time series
von: Binrong Wu, et al.
Veröffentlicht: (2024)

Data‐Driven Prediction of Climate Variables in Agricultural Cities of India With Hybrid GA‐TCN‐LSTM Model
von: Anil Utku
Veröffentlicht: (2025)

Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network
von: Kang Wang, et al.
Veröffentlicht: (2024)

Forecasting Corporate Default Risk Across Multiple Horizons With Interpretable Machine Learning
von: Qingli Dong, et al.
Veröffentlicht: (2026)

Robust Estimation of Multivariate Time Series Data Based on Reduced Rank Model
von: Tengteng Xu, et al.
Veröffentlicht: (2024)

Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis
von: Pavan Kumar Nagula, et al.
Veröffentlicht: (2025)

Using DSGE and Machine Learning to Forecast Public Debt for France
von: Emmanouil Sofianos, et al.
Veröffentlicht: (2026)