Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis

In this study, the denoising effect on the performance of prediction models is evaluated. The 13-year daily data (2002–2015) of hydrological time series for the sub-basin of Parishan Lake of the Helle Basin in Iran were used to predict time series. At first, based on observational precipitation and...

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Autores principales:	Farhang Rahmani, Mohammad Hadi Fattahi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural network autoregressive integrated moving average chaos grid partitioning anfis quadratic exponential smoothing radial basis function Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/387fcb464e2f47c7a9c4095d681fa3ba
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id	oai:doaj.org-article:387fcb464e2f47c7a9c4095d681fa3ba
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spelling	oai:doaj.org-article:387fcb464e2f47c7a9c4095d681fa3ba2021-11-05T19:01:40ZInvestigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis2040-22442408-935410.2166/wcc.2020.014https://doaj.org/article/387fcb464e2f47c7a9c4095d681fa3ba2021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1614https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In this study, the denoising effect on the performance of prediction models is evaluated. The 13-year daily data (2002–2015) of hydrological time series for the sub-basin of Parishan Lake of the Helle Basin in Iran were used to predict time series. At first, based on observational precipitation and temperature data, the prediction was performed, using the ARIMA, ANN-MLP, RBF, QES, and GP prediction models (the first scenario). Next, time series were denoised using the wavelet transform method, and then the prediction was made based on the denoised time series (the second scenario). To investigate the performance of the models in the first and second scenarios, nonlinear dynamic and statistical analysis, as well as chaos theory, was used. Finally, the analysis results of the second scenario were compared with those of the first scenario. The comparison revealed that denoising had a positive impact on the performance of all the models. However, it had the least influence on the GP model. In the time series produced by all the models, the error rate, embedding dimension needed to describe the attractors in dynamical systems and entropy decreased, and the correlation and autocorrelation increased. HIGHLIGHTS Conducting nonlinear dynamic and statistical analyses, as well as a chaotic analysis of the performance of the models.; Performing nonlinear dynamic and chaotic analyses of denoising influences on the performance of ARIMA, QES, GP, RBF, and ANN-MLP; Carrying out a statistical analysis of denoising impacts on the performance of forecasting models and comparing the results;Farhang RahmaniMohammad Hadi FattahiIWA Publishingarticleartificial neural networkautoregressive integrated moving averagechaosgrid partitioning anfisquadratic exponential smoothingradial basis functionEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1614-1630 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network autoregressive integrated moving average chaos grid partitioning anfis quadratic exponential smoothing radial basis function Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	artificial neural network autoregressive integrated moving average chaos grid partitioning anfis quadratic exponential smoothing radial basis function Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Farhang Rahmani Mohammad Hadi Fattahi Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
description	In this study, the denoising effect on the performance of prediction models is evaluated. The 13-year daily data (2002–2015) of hydrological time series for the sub-basin of Parishan Lake of the Helle Basin in Iran were used to predict time series. At first, based on observational precipitation and temperature data, the prediction was performed, using the ARIMA, ANN-MLP, RBF, QES, and GP prediction models (the first scenario). Next, time series were denoised using the wavelet transform method, and then the prediction was made based on the denoised time series (the second scenario). To investigate the performance of the models in the first and second scenarios, nonlinear dynamic and statistical analysis, as well as chaos theory, was used. Finally, the analysis results of the second scenario were compared with those of the first scenario. The comparison revealed that denoising had a positive impact on the performance of all the models. However, it had the least influence on the GP model. In the time series produced by all the models, the error rate, embedding dimension needed to describe the attractors in dynamical systems and entropy decreased, and the correlation and autocorrelation increased. HIGHLIGHTS Conducting nonlinear dynamic and statistical analyses, as well as a chaotic analysis of the performance of the models.; Performing nonlinear dynamic and chaotic analyses of denoising influences on the performance of ARIMA, QES, GP, RBF, and ANN-MLP; Carrying out a statistical analysis of denoising impacts on the performance of forecasting models and comparing the results;
format	article
author	Farhang Rahmani Mohammad Hadi Fattahi
author_facet	Farhang Rahmani Mohammad Hadi Fattahi
author_sort	Farhang Rahmani
title	Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
title_short	Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
title_full	Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
title_fullStr	Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
title_full_unstemmed	Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
title_sort	investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/387fcb464e2f47c7a9c4095d681fa3ba
work_keys_str_mv	AT farhangrahmani investigationofdenoisingeffectsonforecastingmodelsbystatisticalandnonlineardynamicanalysis AT mohammadhadifattahi investigationofdenoisingeffectsonforecastingmodelsbystatisticalandnonlineardynamicanalysis
_version_	1718444031301648384

Investigation of denoising effects on forecasting models by statistical and nonlinear dynamic analysis

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