… with state membership degree, and the wavelet neural network. The ARIMA-GARCH model
is used to predict the similar and volatile parts, and the other methods are adopted to predict …

… It can be seen that it is crucial to find the right model or method to predict the traffic flow. …
model and GARCH-M model, the ARIMA-GARCH-M hybrid model is constructed to predict the …

… was investigated whether one is good enough as both orders for financial issues [57] and
the orders were set to one in [20], where ARIMA/GARCH model was adapted for network traffic …

… Conditional Heteroscedasticity (ARIMA-GARCH) was used for traffic flow prediction in
short-term time series that exhibit stationarity characteristics. However, the approach failed to …

… The research in [33] uses an ARIMA/GARCH combination for network traffic modelling and
prediction. In this approach linear terms in the time series are captured by ARIMA model and …

… ARIMA model to be sufficient to forecast traffic flow. Bhardwaj et al. (2014) while predicting the
… of GARCH (1,1) is a fitted model forecast, since RMSE, MAE, and MAPE is low, and further …

… To compare forecasting results with the real value, with forecasting … the forecasting MAPE
and RMSE of the ARIMA-GARCH model are 0.045% and 0.071, and those of the ARIMA model …

… -term memory neural network. In addition, Yao et al. [35] presented a nonlinear hybrid method
for traffic flow forecasting, which uses the ARIMA-GARCH model to predict the similar and …

… This paper presents the estimation process in developing the best fitted ARIMA-GARCH
model to generate forecast values of the S&P 500 stock prices. We have shown the method we …

… To fortify the forecasting model, this paper proposes a Hybrid ARIMA-GARCH model,
incorporating the assets of ARIMA and GARCH model, for forecasting of wind power. The proposed …