  Many applications in different domains produce large amount of time series
data. Making accurate forecasting is critical for many decision makers. Various
time series forecasting methods exist which use linear and nonlinear models
separately or combination of both. Studies show that combining of linear and
nonlinear models can be effective to improve forecasting performance. However,
some assumptions that those existing methods make, might restrict their
performance in certain situations. We provide a new Autoregressive Integrated
Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that work
in a more general framework. Experimental results show that strategies for
decomposing the original data and for combining linear and nonlinear models
throughout the hybridization process are key factors in the forecasting
performance of the methods. By using appropriate strategies, our hybrid method
can be an effective way to improve forecasting accuracy obtained by traditional
hybrid methods and also either of the individual methods used separately.
