A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting

Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, d...

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Main Authors: Hanapi, A.L.M., Othman, M., Sokkalingam, R., Ramli, N., Husin, A., Vasant, P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23358 /
Published: MDPI AG 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082703763&doi=10.3390%2fapp10061949&partnerID=40&md5=6b7a451733670f0baef0cba392e3496d
http://eprints.utp.edu.my/23358/
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spelling utp-eprints.233582021-08-19T07:23:49Z A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting Hanapi, A.L.M. Othman, M. Sokkalingam, R. Ramli, N. Husin, A. Vasant, P. Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on dierent eects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting. © 2020 by the authors. MDPI AG 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082703763&doi=10.3390%2fapp10061949&partnerID=40&md5=6b7a451733670f0baef0cba392e3496d Hanapi, A.L.M. and Othman, M. and Sokkalingam, R. and Ramli, N. and Husin, A. and Vasant, P. (2020) A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting. Applied Sciences (Switzerland), 10 (6). http://eprints.utp.edu.my/23358/
institution Universiti Teknologi Petronas
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description Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on dierent eects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting. © 2020 by the authors.
format Article
author Hanapi, A.L.M.
Othman, M.
Sokkalingam, R.
Ramli, N.
Husin, A.
Vasant, P.
spellingShingle Hanapi, A.L.M.
Othman, M.
Sokkalingam, R.
Ramli, N.
Husin, A.
Vasant, P.
A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
author_sort Hanapi, A.L.M.
title A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
title_short A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
title_full A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
title_fullStr A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
title_full_unstemmed A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
title_sort novel fuzzy linear regression slidingwindow garch model for time-series forecasting
publisher MDPI AG
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082703763&doi=10.3390%2fapp10061949&partnerID=40&md5=6b7a451733670f0baef0cba392e3496d
http://eprints.utp.edu.my/23358/
_version_ 1741196663052042240
score 11.62408

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