eprintid: 14115
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/41/15
datestamp: 2023-11-10 03:28:41
lastmod: 2023-11-10 03:28:41
status_changed: 2023-11-10 01:56:03
type: article
metadata_visibility: show
creators_name: Alyousifi, Y.
creators_name: Othman, M.
creators_name: Husin, A.
creators_name: Rathnayake, U.
title: A new hybrid fuzzy time series model with an application to predict PM10 concentration
ispublished: pub
keywords: atmospheric pollution; concentration (composition); forecasting method; Markov chain; numerical model; particulate matter; probability; time series analysis, article; forecasting; Markov chain; Melaka; particulate matter 10; prediction; probability; Taiwan; time series analysis; air pollution; algorithm; cluster analysis; forecasting; fuzzy logic, Malaysia; Melaka; West Malaysia, Air Pollution; Algorithms; Cluster Analysis; Forecasting; Fuzzy Logic; Markov Chains
note: cited By 14
abstract: 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. © 2021 The Authors
date: 2021
publisher: Academic Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117954095&doi=10.1016%2fj.ecoenv.2021.112875&partnerID=40&md5=31d7ea5f418f2c048513d5243ac8cdad
id_number: 10.1016/j.ecoenv.2021.112875
full_text_status: none
publication: Ecotoxicology and Environmental Safety
volume: 227
refereed: TRUE
issn: 01476513
citation:   Alyousifi, Y. and Othman, M. and Husin, A. and Rathnayake, U.  (2021) A new hybrid fuzzy time series model with an application to predict PM10 concentration.  Ecotoxicology and Environmental Safety, 227.   ISSN 01476513