| dc.description.abstract | Autoregressive Integrated Moving Average models (ARIMA) is a popular choice of
many statisticians or corporate forecasters in financial forecasting during the past
three decades. However, the drawback of ARIMA models is that they assume the
future values of a time series have a linear relationship with current and past values
as well as with white noise, so using ARIMA models may not be suitable for complex nonlinear problems. Then Artificial Neural Network (ANN) models have been
developed and become an alternative to overcome the limitation of the linear time
series models, nevertheless their performance in some situations is inconsistent. In
this study, we propose a hybrid model which is a combination of both ARIMA and
ANN to satisfy the real-world time series that is generally contains both linear and
nonlinear patterns for improving forecasting efficiency. | en_US |
