A new hybrid fuzzy time series model with an application to predict PM10 concentration

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forec...

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Autores principales:	Yousif Alyousifi, Mahmod Othman, Abdullah Husin, Upaka Rathnayake
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	C-Means clustering technique Forecasting Fuzzy relationship group Fuzzy time series (FTS) Markov transition probability matrix Environmental pollution TD172-193.5 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/89cea552632c48a8a3bb90073d4b051c
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spelling	oai:doaj.org-article:89cea552632c48a8a3bb90073d4b051c2021-11-06T04:14:10ZA new hybrid fuzzy time series model with an application to predict PM10 concentration0147-651310.1016/j.ecoenv.2021.112875https://doaj.org/article/89cea552632c48a8a3bb90073d4b051c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0147651321009878https://doaj.org/toc/0147-6513Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.Yousif AlyousifiMahmod OthmanAbdullah HusinUpaka RathnayakeElsevierarticleC-Means clustering techniqueForecastingFuzzy relationship groupFuzzy time series (FTS)Markov transition probability matrixEnvironmental pollutionTD172-193.5Environmental sciencesGE1-350ENEcotoxicology and Environmental Safety, Vol 227, Iss , Pp 112875- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	C-Means clustering technique Forecasting Fuzzy relationship group Fuzzy time series (FTS) Markov transition probability matrix Environmental pollution TD172-193.5 Environmental sciences GE1-350
spellingShingle	C-Means clustering technique Forecasting Fuzzy relationship group Fuzzy time series (FTS) Markov transition probability matrix Environmental pollution TD172-193.5 Environmental sciences GE1-350 Yousif Alyousifi Mahmod Othman Abdullah Husin Upaka Rathnayake A new hybrid fuzzy time series model with an application to predict PM10 concentration
description	Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
format	article
author	Yousif Alyousifi Mahmod Othman Abdullah Husin Upaka Rathnayake
author_facet	Yousif Alyousifi Mahmod Othman Abdullah Husin Upaka Rathnayake
author_sort	Yousif Alyousifi
title	A new hybrid fuzzy time series model with an application to predict PM10 concentration
title_short	A new hybrid fuzzy time series model with an application to predict PM10 concentration
title_full	A new hybrid fuzzy time series model with an application to predict PM10 concentration
title_fullStr	A new hybrid fuzzy time series model with an application to predict PM10 concentration
title_full_unstemmed	A new hybrid fuzzy time series model with an application to predict PM10 concentration
title_sort	new hybrid fuzzy time series model with an application to predict pm10 concentration
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/89cea552632c48a8a3bb90073d4b051c
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A new hybrid fuzzy time series model with an application to predict PM10 concentration

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