TY - CHAP

T1 - Nonlinear forecasting with a hybrid approach combining SARIMA, ARCH and ANN

AU - Egrioglu, Erol

AU - Aladag, Cagdas Hakan

AU - Kadilar, Cem

PY - 2011/12/1

Y1 - 2011/12/1

N2 - Time series forecasting is a vital issue for many institutions. In the literature, many researchers from various disciplines have tried to improve forecasting models to reach more accurate forecasts. It is known that real life time series has a nonlinear structure in general. Therefore, conventional linear methods are insufficient for real life time series. Some methods such as autoregressive conditional heteroskedastiacity (ARCH) and artificial neural networks (ANN) have been employed to forecast nonlinear time series. ANN has been successfully used for forecasting nonlinear time series in many implementations since ANN can model both the linear and nonlinear parts of the time series. In this study, a novel hybrid forecasting model combining seasonal autoregressive integrated moving average (SARIMA), ARCH and ANN methods is proposed to reach high accuracy level for nonlinear time series. It is presented how the proposed hybrid method works and in the implementation, the proposed method is applied to the weekly rates of TL/USD series between the period January 3, 2005 and January 28, 2008. This time series is also forecasted by using other approaches available in the literature for comparison. Finally, it is seen that the proposed hybrid approach has better forecasts than those calculated from other methods.

AB - Time series forecasting is a vital issue for many institutions. In the literature, many researchers from various disciplines have tried to improve forecasting models to reach more accurate forecasts. It is known that real life time series has a nonlinear structure in general. Therefore, conventional linear methods are insufficient for real life time series. Some methods such as autoregressive conditional heteroskedastiacity (ARCH) and artificial neural networks (ANN) have been employed to forecast nonlinear time series. ANN has been successfully used for forecasting nonlinear time series in many implementations since ANN can model both the linear and nonlinear parts of the time series. In this study, a novel hybrid forecasting model combining seasonal autoregressive integrated moving average (SARIMA), ARCH and ANN methods is proposed to reach high accuracy level for nonlinear time series. It is presented how the proposed hybrid method works and in the implementation, the proposed method is applied to the weekly rates of TL/USD series between the period January 3, 2005 and January 28, 2008. This time series is also forecasted by using other approaches available in the literature for comparison. Finally, it is seen that the proposed hybrid approach has better forecasts than those calculated from other methods.

KW - ARCH models

KW - Artificial neural networks

KW - Exchange rates

KW - Forecasting

KW - Nonlinearity

KW - Time series

M3 - Chapter

AN - SCOPUS:84895338526

SN - 9781613242865

SP - 221

EP - 228

BT - New Developments in Artificial Neural Networks Research

PB - Nova Science Publishers, Inc.

ER -