A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting DOI
Liling Zeng, Huanling Hu, Qingkui Song

et al.

Energy, Journal Year: 2024, Volume and Issue: 313, P. 133699 - 133699

Published: Nov. 2, 2024

Language: Английский

Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values DOI Creative Commons
Vule Mizdraković, Maja Kljajić, Miodrag Živković

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 299, P. 112026 - 112026

Published: June 6, 2024

Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research examines time series data of various exogenous endogenous factors: Bitcoin, Ethereum, S&P 500, VIX closing prices; exchange rates the Euro GPB to USD; number Bitcoin-related tweets per day. A period three years (from September 2019 2022) is covered by dataset. two-layer framework introduced tasked with accurately forecasting price. In first layer, account for complexities in analyzed data, variational mode decomposition (VMD) extracts trends from series. second Long short-term memory hybrid Bidirectional long networks were used forecast prices several steps ahead. work also an enhanced variant sine cosine algorithm tune control parameters VMD neural attaining best possible performance. The main focus combining modified metaheuristics improve cryptocurrency value forecast. Two sets experiments conducted, without VMD. results have been contrasted models tuned seven other cutting-edge optimizers. Extensive experimental outcomes indicate can be forecasted great accuracy using selected decomposition. Additionally, model was analyzed, Shapley values indicated such as EUR/USD rates, Ethereum prices, GBP/USD a significant impact forecasts.

Language: Английский

Citations

17
A multi-scale analysis method with multi-feature selection for house prices forecasting DOI
Jin Shao, Lean Yu, Nengmin Zeng

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112779 - 112779

Published: Jan. 1, 2025

Language: Английский

Citations

2
A dual decomposition integration and error correction model for carbon price prediction DOI
Yanan Li, Xinsheng Zhang, Minghu Wang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124035 - 124035

Published: Jan. 10, 2025

Language: Английский

Citations

0
MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting DOI
Zhirui Tian, Wei Sun, Chenye Wu

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125330 - 125330

Published: Jan. 15, 2025

Language: Английский

Citations

0
A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets DOI
Jinchao Li, Yuwei Guo

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124237 - 124237

Published: Jan. 29, 2025

Language: Английский

Citations

0
Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data DOI Creative Commons
Spyros Giannelos, Danny Pudjianto,

Tai Zhang

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1712 - 1712

Published: March 29, 2025

Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods modeling uncertainties in load demand solar generation, ranging from normal distribution assumptions to distributions sourced CityLearn 2.3.0. We also implement kernel density estimation (KDE) represent the non-parametric characteristics of actual data. Through Monte Carlo simulation, we emphasize value robust, data-driven methodologies optimizing energy hub operations under realistic uncertainty conditions effectively conducting risk assessment. The real-world data confirms that non-Gaussian nature building-level PV output is most accurately represented through KDE, leading more precise cost projections considered hub.

Language: Английский

Citations

0
A multi-objective ensemble prediction model for interval-valued carbon price based on mixed-frequency data and sub-model selection DOI
Jinpei Liu, Jiaqi Wang,

Xiaoman Zhao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136309 - 136309

Published: April 1, 2025

Language: Английский

Citations

0
Research on the Construction and Optimization of Physical Education Teaching Analysis Platform Based on Bi-LSTM Model DOI Creative Commons
Y. Li

Systems and Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 200265 - 200265

Published: April 1, 2025

Language: Английский

Citations

0
A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN DOI

Xiwen Cui,

Xiaodan Zhang, Dongxiao Niu

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112311 - 112311

Published: Oct. 10, 2024

Language: Английский

Citations

1
An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression DOI
Yuxuan Lu,

Jujie Wang,

Qian Li

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 692, P. 121651 - 121651

Published: Nov. 16, 2024

Language: Английский

Citations

1