Prediction of Next-Day Stock Price Using Stacked Ensemble Learning Techniques—An Exploration of Model Compatibility DOI

Amal Joseph,

Bansi Pambhar,

Allen George

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 253 - 265

Опубликована: Янв. 1, 2025

Язык: Английский

Localized global models using autoencoder-based clustering to forecast related time series DOI
Hossein Abbasimehr, Ali Noshad

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Язык: Английский

Процитировано

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

Huanze Zeng,

Huanling Hu

и другие.

Applied Intelligence, Год журнала: 2025, Номер 55(6)

Опубликована: Фев. 12, 2025

Процитировано

1
Prediction of Waste Sludge Production in Municipal Wastewater Treatment Plants by Deep-Learning Algorithms with Antioverfitting Strategies DOI
Juanjuan Chen, Weixiang Chao, Yixuan Wang

и другие.

ACS ES&T Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Язык: Английский

Процитировано

0
Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies DOI Creative Commons
Mohiuddin Ahmed Bhuiyan, Md. Oliullah Rafi,

Gourab Nicholas Rodrigues

и другие.

Array, Год журнала: 2025, Номер unknown, С. 100390 - 100390

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Active learning regression quality prediction model and grinding mechanism for ceramic bearing grinding processing DOI Creative Commons

Longfei Gao,

Yuhou Wu, Jian Sun

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0320494 - e0320494

Опубликована: Апрель 7, 2025

The study aims to explore quality prediction in ceramic bearing grinding processing, with particular focus on the effect of parameters surface roughness. uses active learning regression model for construction and optimization, empirical analysis under different conditions. At same time, various deep models are utilized conduct experiments processing. experimental setup covers a variety parameters, including wheel linear speed, depth feed rate, ensure accuracy reliability According results, when increases 21 μm, average training loss further decreases 0.03622, roughness Ra value significantly 0.1624 μm. In addition, experiment also found that increasing velocity moderately adjusting can improve machining quality. For example, is 45 m/s 0.015 mm, drops 0.1876 results not only provide theoretical support processing bearings, but basis optimization actual production, which has an important industrial application value.

Язык: Английский

Процитировано

0
A Feature Optimized Attention Transformer with Kinetic Information Capture and Weighted Robust Z-score for Industrial NOx Emission Forecasting DOI
Jian Long, Siyu Jiang, Luyao Wang

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 136276 - 136276

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
StoDEMO-PAE: A stochastic derivative-free multi-error-optimized performer autoencoder for air quality anomaly detection and explainable spatiotemporal tracing DOI
Xiliang Liu, Xiaoying Zhi, Jiadi Luo

и другие.

GeoInformatica, Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0
Implementation of deep learning algorithms to model agricultural drought towards sustainable land management in Namibia's Omusati region DOI

Selma Ndeshimona Iilonga,

Oluibukun Gbenga Ajayi

Land Use Policy, Год журнала: 2025, Номер 156, С. 107593 - 107593

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

0
Prediction of Next-Day Stock Price Using Stacked Ensemble Learning Techniques—An Exploration of Model Compatibility DOI

Amal Joseph,

Bansi Pambhar,

Allen George

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 253 - 265

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0