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

Evaluation of precipitation forecasting base on GraphCast over mainland China DOI Creative Commons

Zihuang Yan,

Xianghui Lu, Lifeng Wu

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Abstract The accurate cumulative precipitation forecasts are essential for monitoring water resources and natural disasters. Thecombination of deep learning big data has become a new direction forecasting. However, the currentlarge models still lacking in-situ verification. To accomplish this goal, forecasting performance astate-of-the-art model GraphCast was evaluated. Using from 2393 observation stations the1-3 day period as reference, we assessed in mainland China region 1-3 from2020 to 2021, utilizing high-resolution with 0.25◦×0.25◦ grid spacing 37 layers parameters. ofEuropean Centre Medium-Range Weather Forecasts (ECMWF) also compared. results show that: (1) During the2020-2021 period, 1-day, 2-day, 3-day forecasts, Root Mean Square Error (RMSE)values were primarily between 0.46 9.38 mm/d, 0.44 9.06 respectively. TheMean (ME) values mainly −0.595 1.705 (0.01 mm). (2) As forecast extends, forecastingcapability declines. (3) In various China,GraphCast demonstrates higher predictive accuracy than ECMWF. (4) Compared ECMWF, demonstrated thebest warm-temperate humid sub-humid north China, RMSE being approximately 12%higher. Our study indicates that significant potential

Language: Английский

Citations

1
Prediction of Summer Precipitation Via Machine Learning with Key Climate Variables:A Case Study in Xinjiang, China DOI

Chenzhi Ma,

Junqiang Yao,

Yinxue Mo

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

1
Future Estimation of Hydrometeorological Variables Using Machine Learning Techniques: A Comparative Approach DOI
Jean Firmino Cardoso, Erickson Johny Galindo da Silva, Ialy Rayane de Aguiar Costa

et al.

Revista de Gestão Social e Ambiental, Journal Year: 2024, Volume and Issue: 18(6), P. e08267 - e08267

Published: Aug. 1, 2024

Objective: The objective of the research was to analyze and compare different machine learning models identify which technique presents best performance in predicting hydrometeorological variables. Theoretical Framework: This section main concepts that underpin work. Machine techniques such as support vector machines, decision trees, random forests, artificial neural networks, gradient boosting are presented, providing a solid foundation for understanding context investigation. Method: study uses comparative methodology by applying predict variables based on data collected Petrolina-PE. Various were employed compared. Data normalization performed through logarithms, treatment included filling or excluding inconsistent records. effectiveness is evaluated using metrics Nash-Sutcliffe efficiency coefficient, Willmott index, Pearson correlation coefficient. Results Discussion: obtained results showed good predictability, ranging from 50 70% efficiency. analysis allowed identifying patterns relationships between initial configurations algorithms, contributing better processes their predictability. Research Implications: By more accurate reliable forecasts, presented can assist managers making decisions about sustainable use water mitigation natural disasters floods. Originality/Value: contributes literature advancing estimation variables, improving existing techniques, resource management. Its impact extends mitigating risks associated with extreme hydrological events promoting resources, sustainability resilience aquatic ecosystems, essential face climate change environmental challenges.

Language: Английский

Citations

1
Prediction of summer precipitation via machine learning with key climate variables：A case study in Xinjiang, China DOI Creative Commons

Chenzhi Ma,

Junqiang Yao,

Yinxue Mo

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 56, P. 101964 - 101964

Published: Sept. 16, 2024

Language: Английский

Citations

1
Aprendizaje por refuerzo como soporte a la predicción de la precipitación mensual. Caso de estudio: Departamento de Boyacá - Colombia DOI Creative Commons
Jimmy Alejandro Zea Gutiérrez, Marco Javier Suárez Barón, Juan-Sebastián González-Sanabria

et al.

TecnoLógicas, Journal Year: 2024, Volume and Issue: 27(60), P. e3017 - e3017

Published: June 27, 2024

La emisión de gases efecto invernadero, atribuida directa o indirectamente a la actividad humana, es principal causa del cambio climático nivel mundial. Entre los emitidos, el dióxido carbono (CO2) que más contribuye variación espacio temporal magnitudes físicas como humedad relativa, presión atmosférica, temperatura ambiente y, manera significativa, precipitación. El objetivo investigación fue presentar un análisis predicción precipitación mensual en departamento Boyacá mediante uso modelos basados aprendizaje reforzado (RL, por sus siglas inglés). metodología empleada consistió extraer datos desde CHIRPS 2,0 (Climate Hazards Group InfraRed Precipitation with Station data, versión 2,0) con una resolución espacial 0,05° posteriormente fueron preprocesados para implementación enfoques simulación Montecarlo y profundo (DRL, inglés) proporcionar predicciones mensual. Los resultados obtenidos demostraron DRL generan significativas Es esencial reconocer convencionales Aprendizaje profundo, Memoria Corto Plazo (LSTM) Redes Convolucionales (ConvLSTM), pueden superar términos precisión predicción. Se concluye técnicas refuerzo detecta patrones información ser usados soporte estrategias dirigidas mitigar riesgos económicos sociales derivados fenómenos climáticos.

Citations

0
Feature Importance in Machine Learning with Explainable Artificial Intelligence (XAI) for Rainfall Prediction DOI Creative Commons
Mehul Patel, Ankit Shah

ITM Web of Conferences, Journal Year: 2024, Volume and Issue: 65, P. 03007 - 03007

Published: Jan. 1, 2024

Precipitation expectation is a pivotal subject for the administration of water assets and counteraction hydrological calamities. To make precipitation forecast find essential elements influencing precipitation, this study presents logical profound learning approach in two sections. The initial segment with consideration system which could foresee while second part clarification figures attribution values information weather conditions to evaluate their significance. A contextual investigation led on hourly India’s population wise top eight urban cities. outcomes predominantly demonstrate that main whose component esteem adversely/decidedly corresponded its esteem. review’s importance lies upgrading giving interpretability through recognizable proof persuasive variables, works long haul arranging more comprehension mind-boggling climate frameworks.

Language: Английский

Citations

0
A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland DOI Creative Commons

Menatallah Abdel Azeem,

Soumyabrata Dev

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 12, P. 100515 - 100515

Published: Aug. 24, 2024

Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance interpretability of machine learning (ML) models rainfall in Republic Ireland. The study uses a brute force approach Leave One Feature Out (LOFO) methodology to evaluate model under highly correlated variables. Results reveal consistent across ML algorithms, with average Area Under Curve Precision-Recall (AUC-PR) scores ranging from 0.987 1.000, certain features such as atmospheric pressure soil moisture deficits demonstrating significant influence on outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming significance prediction. underscores importance selection enhancing accuracy usability

Language: Английский

Citations

0
Use of GPCC and GPCP Precipitation Products and GRACE and GRACE-FO Terrestrial Water Storage Observations for the Assessment of Drought Recovery Times DOI Creative Commons
Çağatay Çakan, M. Tuğrul Yılmaz, Henryk Dobslaw

et al.

Published: Sept. 3, 2024

Abstract. Meteorological and hydrological processes depend on accurate precipitation observations. Most products utilize station-based observations directly or to bias correct satellite retrievals. Thus, the validation of requires further independent data. This study aims assess accuracy Global Precipitation Climatology Center (GPCC) Project (GPCP) by estimating drought recovery time (DRT) from terrestrial water storage anomaly (TWSA) acquired gravimetry required amount across five main Köppen-Geiger climate zones. Station-based products, namely GPCC Full Data Monthly Product v2022 GPCP v3.2 Analysis Product, were utilized estimate DRT. Additionally, JPL mascon G3P Total Water Storage (TWS) monthly-solutions Gravity Recovery Climate Experiment (GRACE) GRACE Follow-On (GRACE-FO) missions also employed for DRT estimation. was estimated through following two methods: (1) deficit, determined as negative residual detrended TWSA its climatology, (2) amount, derived linear relationship between cumulative smoothed (cdPA) TWSA. The results show no significant differences in mean estimations using GPCP. Conversely, estimation is 2.6 months longer average than that G3P. equatorial zone showed shortest estimation, 10.3 months, while polar had longest, 16.2 months. Except zone, arid shows highest estimations, 13.9 Consistency methods high different zones, with exhibiting highest, 97.8 %, lowest, 74.9 %. Similar results, consistency not obtained In contrast, approximately 5.0 % higher mascon. findings based indicate a close agreement Moreover, more consistent These provide necessary information product characteristics, which helps understanding meteorological processes.

Language: Английский

Citations

0
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

Citations

0