Explainable Soybean Futures Price Forecasting Based on Multi‐Source Feature Fusion DOI Open Access
Binrong Wu, Sihao Yu,

Sheng‐Xiang Lv

et al.

Journal of Forecasting, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

ABSTRACT The prediction and early warning of soybean futures prices have been even more crucial for the formulation food‐related policies trade risk management. Amid increasing geopolitical conflicts uncertainty in across countries recent years, there significant fluctuations global prices, making it necessary to investigate reveal price determination mechanism, accurately predict trends future prices. Therefore, this study proposes a comprehensive interpretable framework forecasting. Specifically, employs set methodologies. Using snow ablation optimizer (SAO), improves parameters time fusion transformer (TFT) model, an advanced predictive model based on self‐attention mechanism. Besides, addresses factors influencing constructs effective features through feature method. To explore volatility trends, original series are decomposed using variational mode decomposition (VMD). This also enhances accuracy predictions by introducing coefficients trading volumes as predictors. empirical findings suggest that VMD‐SAO‐TFT interpretability, offering implications decision‐makers achieve accurate agricultural

Language: Английский

Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S DOI
Xiaoying Sun, Haizhong Liu

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132228 - 132228

Published: Oct. 1, 2024

Language: Английский

Citations

24
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach DOI Creative Commons
Georgios Tsoumplekas, Christos L. Athanasiadis, Dimitrios I. Doukas

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 742 - 742

Published: Feb. 6, 2025

Despite the rapid expansion of smart grids and large volumes data at individual consumer level, there are still various cases where adequate collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term in context few-shot learning. Specifically, proposed method can rapidly adapt generalize within any unknown time series arbitrary length using only minimal training samples. In this context, meta-learning model learns optimal set initial parameters a base-level learner recurrent neural network. The evaluated dataset historical consumption from real-world consumers. examined series’ short length, it produces forecasts outperforming transfer learning task-specific machine methods by 12.5%. To enhance robustness fairness during evaluation, novel metric, mean average log percentage error, that alleviates bias introduced commonly used MAPE metric. Finally, studies evaluate model’s under different hyperparameters lengths also conducted, demonstrating approach consistently outperforms all other models.

Language: Английский

Citations

1
A novel data-driven model for explainable hog price forecasting DOI
Binrong Wu,

Huanze Zeng,

Huanling Hu

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: Feb. 12, 2025

Citations

1
Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition DOI

Zhiyuan Leng,

Chen Lü, Bin Yi

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135551 - 135551

Published: March 1, 2025

Language: Английский

Citations

1
Statistical Comparison of Time Series Models for Forecasting Brazilian Monthly Energy Demand Using Economic, Industrial, and Climatic Exogenous Variables DOI Creative Commons
André Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Patrícia Helena dos Santos Martins

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5846 - 5846

Published: July 4, 2024

Energy demand forecasting is crucial for effective resource management within the energy sector and aligned with objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis different models to predict future trends in Brazil, improve methodologies, achieve sustainable development goals. The evaluation encompasses following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, components (TBATS), draws attention their respective strengths limitations. Its findings reveal unique capabilities among models, excelling tracing seasonal patterns, FB demonstrating its potential applicability across various sectors, Holt–Winters adept at managing fluctuations, TBATS offering flexibility albeit requiring significant data inputs. Additionally, investigation explores effect external factors on consumption, by establishing connections through Granger causality test conducting correlation analyses. accuracy these assessed without exogenous variables, categorized as economical, industrial, climatic. Ultimately, this seeks add body knowledge prediction, well allow informed decision-making planning policymaking and, thus, make rapid progress toward SDG7 associated targets. paper concludes that, although achieves best accuracy, most fit model, considering residual autocorrelation, it predicts that Brazil will approximately 70,000 GWh 2033.

Language: Английский

Citations

7
A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection DOI
Zhihao Shang, Yanhua Chen,

Daokai Lai

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124560 - 124560

Published: June 27, 2024

Language: Английский

Citations

5
A synchronized multi-step wind speed prediction with adaptive features and parameters selection: Insights from an interaction model DOI
Wenxin Xia, Jinxing Che, Kun Hu

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124764 - 124764

Published: July 14, 2024

Language: Английский

Citations

5
Interpretable wind power forecasting combing seasonal-trend representations learning with temporal fusion transformers architecture DOI
Zhewen Niu, Xiaoqing Han,

Dongxia Zhang

et al.

Energy, Journal Year: 2024, Volume and Issue: 306, P. 132482 - 132482

Published: July 17, 2024

Language: Английский

Citations

5
Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers DOI
Binrong Wu,

Huanze Zeng,

Zhongrui Wang

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111639 - 111639

Published: April 21, 2024

Language: Английский

Citations

4
Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer DOI Creative Commons
Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad

et al.

Energy Science & Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

ABSTRACT Forecasting green energy is crucial in diminishing dependence on fossil fuels and fostering sustainable development. However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, necessity for precise long‐term projections. This study thoughtfully examines these issues using temporal fusion transformer (TFT) model to project production across five Latin American nations (Argentina, Brazil, Chile, Colombia, Mexico) Canada, drawing from 1965 2023. The performance proposed TFT more authentic compared with gated recurrent unit (GRU), long short‐term memory (LSTM), deep autoregression (DeepAR), meta graph‐based convolutional network (MegaCRN). has a mean square error (MSE) 0.0003, root (RMSE) 0.0173, absolute (MAE) 0.0112 percentage (MAPE) 1.76%. From preceding results, clear that can identify dynamic patterns will contribute towards achieving development goals by end 2040.

Language: Английский

Citations

0