eprintid: 13394
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/33/94
datestamp: 2023-11-10 03:27:57
lastmod: 2023-11-10 03:27:57
status_changed: 2023-11-10 01:51:04
type: article
metadata_visibility: show
creators_name: Hanapi, A.L.M.
creators_name: Othman, M.
creators_name: Sokkalingam, R.
creators_name: Ramli, N.
creators_name: Husin, A.
creators_name: Vasant, P.
title: A novel fuzzy linear regression slidingwindow GARCH model for time-series forecasting
ispublished: pub
note: cited By 6
abstract: 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.
date: 2020
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082703763&doi=10.3390%2fapp10061949&partnerID=40&md5=6b7a451733670f0baef0cba392e3496d
id_number: 10.3390/app10061949
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 10
number: 6
refereed: TRUE
issn: 20763417
citation:   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).   ISSN 20763417