Title: Empirical comparison of some automatic ARIMA modeling
Authors: Dedi Rosadi - Universitas Gadjah Mada (Indonesia) [presenting]
Abstract: In some application of time series modeling, it is necessary to obtain forecast of various types of data automatically and possibly, in real-time way, for instance, to do a real-time processing of the satellite data. Various automatic algorithms for modeling ARIMA models are available in the literature, where we will discuss four methods in particular. One of the methods is based on a combination between the best exponential smoothing models to obtain the forecast, together with state-space approach of the underlying model to obtain the prediction interval. Other method, which is more advanced method, is based on X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. Two other methods use more heuristic approaches, namely genetic algorithms and the neural networks. These approaches are implemented in our R-GUI package, namely RcmdrPlugin.SPSS. We provide empirical application of the methods and tool using real data.