Login
Register
Reset Password
Publish & Distribute
Publishing Solutions
Distribution Solutions
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Blog
Contact
Search
EUR
USD
GBP
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Journal of Artificial Intelligence and Soft Computing Research
Volume 7 (2017): Issue 1 (January 2017)
Open Access
Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets
Cristian Rodrìguez Rivero
Cristian Rodrìguez Rivero
,
Juliàn Pucheta
Juliàn Pucheta
,
Sergio Laboret
Sergio Laboret
,
Vìctor Sauchelli
Vìctor Sauchelli
and
Daniel Patiǹo
Daniel Patiǹo
| Dec 17, 2016
Journal of Artificial Intelligence and Soft Computing Research
Volume 7 (2017): Issue 1 (January 2017)
About this article
Previous Article
Next Article
Abstract
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Published Online:
Dec 17, 2016
Page range:
5 - 16
DOI:
https://doi.org/10.1515/jaiscr-2017-0001
Keywords
short time series
,
forecasting
,
missing data
,
energy associated to series
,
complete and incomplete datasets
© 2016
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Cristian Rodrìguez Rivero
Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,
Argentina
Juliàn Pucheta
Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,
Argentina
Sergio Laboret
Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,
Argentina
Vìctor Sauchelli
Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,
Argentina
Daniel Patiǹo
Advanced Intelligent Systems Laboratory, Institute of Automatic Universidad Nacional de San JuanSan Juan,
Argentina