Skip to content
Descubridor Institucional UMAR
Inicio
Búsqueda avanzada
Explorar
Inicio
Búsqueda avanzada
Explorar
Login
Language
English
Deutsch
Español
Français
Italiano
All Fields
Title
Author
Subject
Call Number
ISBN/ISSN
Tag
Find
Advanced
An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting
An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting
Fuente:
Wiley Open Access
Saved in:
Bibliographic Details
Main Authors:
Sen Wu
,
Wei Wang
,
Yanan Song
,
Shuaiqi Liu
Format:
Artículo Open Access
Published:
Wiley
2024
Subjects:
Expert Systems
Online Access:
Acceder al recurso
Tags:
Add Tag
No Tags, Be the first to tag this record!
Cite this
Text this
Email this
Print
Export Record
Export to RefWorks
Export to EndNoteWeb
Export to EndNote
Save to List
Permanent link
Holdings
Description
Comments
Similar Items
Staff View
Internet
https://onlinelibrary.wiley.com/doi/10.1111/exsy.13672
Similar Items
KDBI special issue: Explainability feature selection framework application for LSTM multivariate time‐series forecast self optimization
by: Eduardo M. Rodrigues, et al.
Published: (2024)
Prediction of Liaoning province steel import and export trade based on deep learning models
by: Limin Zhang
Published: (2024)
Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data
by: Bahareh Amirshahi, et al.
Published: (2024)
Hybrid deep learning models with data fusion approach for electricity load forecasting
by: Serkan Özen, et al.
Published: (2024)
Autism spectrum disorder identification using multi‐model deep ensemble classifier with transfer learning
by: Lakmini Herath, et al.
Published: (2024)
