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
Establishing diversity in synthetic time series for prediction performance evaluation
Establishing diversity in synthetic time series for prediction performance evaluation
Fuente:
Zenodo
Saved in:
Bibliographic Details
Main Authors:
Bahrpeyma Fouad
,
Roantree Mark
,
Cappellari Paolo
,
Scriney Michael
,
McCarren Andrew
Format:
Recurso digital
Published:
Zenodo
2021
Subjects:
Diversity
Time series
Forecasting
Diverse time series
Time series generation
Synthetic data
Synthetic time series
Time series prediction performance evaluation
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://doi.org/10.5281/zenodo.4455631
Similar Items
Time Series Forecasting of Wind Power using LSTM and GRU Deep Learning Models
by: Pawar, Khushboo, et al.
Published: (2025)
A nonlinear time-series prediction methodology based on neural networks and tracking signals
by: Natália Maria Puggina Bianchesi
Published: (2022)
Predicting cancer cells progression via entropy generation based on AR and ARMA models
by: Modaresi Movahed, Tayebeh, et al.
Published: (2021)
Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais
by: Luiz Moreira Coelho Junior
Published: (2013)
Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems,
by: Adarkwa, Kofi
Published: (2004)