TY  - CONF
N2  - Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is one of the most popular time series models that can be used for time series forecasting. However, the computation of the long run variance in the GARCH model is based on the historical data that does not reflect the influence of the recent variance. This study proposed the sliding window GARCH (SWGARCH) model, which is an enhancement of the GARCH model to overcome the limitation of the variance. The sliding window technique is solely to estimate the variance in the SWGARCH model. A performance evaluation of SWGARCH was performed on Standard and Poor's 500 index dataset and compared with two (2) common time series forecasting models in terms of mean square error and mean absolute percentage error. The experimental results showed that the performance of SWGARCH is superior than GARCH and ARIMA-GARCH, which confirmed that SWGARCH can be used for time series forecasting. © 2017 Association for Computing Machinery.
N1  - cited By 1; Conference of 1st International Conference on Internet of Things and Machine Learning, IML 2017 ; Conference Date: 17 October 2017 Through 18 October 2017; Conference Code:136703
TI  - SWGARCH model for time series forecasting
ID  - scholars8287
KW  - Artificial intelligence; Forecasting; Internet of things; Learning systems; Mean square error
KW  -  GARCH; Generalized autoregressive conditional heteroskedasticity; Long-run variance; Mean absolute percentage error; Sliding Window; Sliding window techniques; Time series forecasting; Time series forecasting models
KW  -  Time series
AV  - none
A1  - Shbier, M.Z.
A1  - Ku-Mahamud, K.R.
A1  - Othman, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048370207&doi=10.1145%2f3109761.3109806&partnerID=40&md5=73b05088eace65827b8ae02b596a1c0a
Y1  - 2017///
PB  - Association for Computing Machinery
SN  - 9781450352437
ER  -