Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios
Sometimes, there are time series segment, it is necessary to reconstruct information from the past, predict information for the future, in this paper a hybrid approach between Artificial Neural Network (ANN), Monte Carlo simulation (MCS) for the reconstruction (and / or prediction) of time series wi...
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INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC.
2018
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Acceso en línea: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013168655&doi=10.1109%2fUIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0110&partnerID=40&md5=0356ddc13d9825c649b7c6c007a2f706 http://dspace.ucuenca.edu.ec/handle/123456789/29235 |
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oai:localhost:123456789-292352018-02-17T11:26:50Z Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios Bermeo Moyano, Henry Vinicio ANOVA ARMAX Autocorrelation Chi-square test Monte Carlo Simulation Neural Network Sometimes, there are time series segment, it is necessary to reconstruct information from the past, predict information for the future, in this paper a hybrid approach between Artificial Neural Network (ANN), Monte Carlo simulation (MCS) for the reconstruction (and / or prediction) of time series with the generation of L-scenarios is proposed, in order to evaluate results from hybrid method, the Chi-square test, analysis of variance (ANOVA), functions of autocorrelation were used, additionally, the forecasting ANN is compared with ARMAX model prediction, results show that the proposed method could reconstruct the past, could predict the future from known time series segment, so that each prediction in a whole period selected generates a scenario, the L-scenarios have high sameness statistical from original information. In the hybrid method, first, artificial neural network is trained with known information, second the statistics for the MCS are estimated, then L-scenarios were generated by MCS in the selected period, these information will serve such as inputs for ANN trained, finally these outputs ANN will be the whole time series within in the chosen period, which it want to be analysed. Toulose 2018-01-11T16:47:48Z 2018-01-11T16:47:48Z 2016-07-18 info:eu-repo/semantics/Article 9781509027705 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013168655&doi=10.1109%2fUIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0110&partnerID=40&md5=0356ddc13d9825c649b7c6c007a2f706 http://dspace.ucuenca.edu.ec/handle/123456789/29235 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0110 en_US instname:Universidad de Cuenca reponame:Repositorio Digital de la Universidad de Cuenca info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/3.0/ec/ INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC. Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016 info:eu-repo/date/embargoEnd/2022-01-01 0:00 |
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ANOVA ARMAX Autocorrelation Chi-square test Monte Carlo Simulation Neural Network |
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ANOVA ARMAX Autocorrelation Chi-square test Monte Carlo Simulation Neural Network Bermeo Moyano, Henry Vinicio Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
description |
Sometimes, there are time series segment, it is necessary to reconstruct information from the past, predict information for the future, in this paper a hybrid approach between Artificial Neural Network (ANN), Monte Carlo simulation (MCS) for the reconstruction (and / or prediction) of time series with the generation of L-scenarios is proposed, in order to evaluate results from hybrid method, the Chi-square test, analysis of variance (ANOVA), functions of autocorrelation were used, additionally, the forecasting ANN is compared with ARMAX model prediction, results show that the proposed method could reconstruct the past, could predict the future from known time series segment, so that each prediction in a whole period selected generates a scenario, the L-scenarios have high sameness statistical from original information. In the hybrid method, first, artificial neural network is trained with known information, second the statistics for the MCS are estimated, then L-scenarios were generated by MCS in the selected period, these information will serve such as inputs for ANN trained, finally these outputs ANN will be the whole time series within in the chosen period, which it want to be analysed. |
author |
Bermeo Moyano, Henry Vinicio |
author_facet |
Bermeo Moyano, Henry Vinicio |
author_sort |
Bermeo Moyano, Henry Vinicio |
title |
Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
title_short |
Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
title_full |
Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
title_fullStr |
Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
title_full_unstemmed |
Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios |
title_sort |
artificial neural network and monte carlo simulation in a hybrid method for time series forecasting with generation of l-scenarios |
publisher |
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC. |
publishDate |
2018 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013168655&doi=10.1109%2fUIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0110&partnerID=40&md5=0356ddc13d9825c649b7c6c007a2f706 http://dspace.ucuenca.edu.ec/handle/123456789/29235 |
_version_ |
1635523465723248640 |
score |
11,871979 |