Forecasting Short Time Series with Missing Data by Means of Energy Associated to Series

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ISSN Print: 2152-7385
ISSN Online: 2152-7393
www.scirp.org/journal/am
E-mail: am@scirp.org

"Forecasting Short Time Series with Missing Data by Means of Energy Associated to Series"
written by Cristian Rodríguez Rivero, Julián Pucheta, Sergio Laboret, Daniel Patiño, Víctor Sauchelli,
published by Applied Mathematics, Vol.6 No.9, 2015

has been cited by the following article(s):

[1]	Artificial Neural Networks for COVID-19 Time Series Forecasting
	Open Journal of Statistics, 2022

[2]	An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability
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[3]	A Novel LSTM for Multivariate Time Series with Massive Missingness
	2020

[4]	Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries
	Journal of Quantitative Economics, 2019

[5]	Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model
	Journal of Quantitative Economics, 2018

[6]	Energy associated tuning method for short-term series forecasting by complete and incomplete datasets
	Journal of Artificial Intelligence and Soft Computing Research, 2017

[7]	Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya
	2016

[8]	Noisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: application to rainfall series
	2016

[1]	An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability Information Fusion, 2021 DOI:10.1016/j.inffus.2021.03.010

[2]	An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability Information Fusion, 2021 DOI:10.1016/j.inffus.2021.03.010

[3]	A Novel LSTM for Multivariate Time Series with Massive Missingness Sensors, 2020 DOI:10.3390/s20102832

[4]	Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model Journal of Quantitative Economics, 2018 DOI:10.1007/s40953-018-0133-8

[5]	Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets Journal of Artificial Intelligence and Soft Computing Research, 2017 DOI:10.1515/jaiscr-2017-0001

[6]	Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2016 DOI:10.1109/LA-CCI.2016.7885702

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