Time Series Forecasting Using a Hybrid ARIMA and ANN Models

By Malik Arif Arrahman

Student of S1 Statistics

Muhammadiyah University of Semarang

The journal, "Time Series Forecasting using a Hybrid ARIMA and ANN Models", by G. Peter Zhang seek to forecast the time series with hybrid methodology. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling.

The problem in this research comes from the following perspectives. 

First, it is often difficult in practice to determine whether a time series under study is generated from a linear or nonlinear underlying process or whether one particular method is more evective than the other in out-of-sample forecasting. 

Second, real-world time series are rarely pure linear or nonlinear. They often contain both linear and nonlinear patterns. 

Third, it is almost universally agreed in the forecasting literature that no single method is best in every situation.

The focus of research in this journal is about the method to be used not to the dataset. So the discussion in this journal is more emphasized to the method, a case of explanation of methods, disadvantages, and advantages when the two methods are combined.

In this paper, authors propose to take a combining approach to time series forecasting. The linear ARIMA model and the nonlinear ANN model are used jointly, aiming to capture different forms of relationship in the time series data. The hybrid model takes advantage of the unique strength of ARIMA and ANN in linear and nonlinear modeling. 

For complex problems that have both linear and nonlinear correlation structures, the combination method can be an evective way to improve forecasting performance.