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I need to compare the accuracy of time series modeling and neural network techniques. As we all know, large data set is needed for neural networks. Since I'm comparing both techniques, I have to consider equal size for both. I have 5 years of daily exchange rates for the analysis.

Time series plot of 5 years daily exchange rates which shows positive and negative trends is reproduced below.

enter image description here

So, my question is: Can I fit a single time series model for the entire data set? If not, what is the procedure to fit time series model to such data? I have heard that a data set can be divided into subsamples. If so what is the method to perform sampling of the original data set?

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This looks like a nonstationary time series. In general if there are no known interventions I would try to model this as a single series. However if interventions are expected to exist that cause the behavior of the time series to change there are models of the Box-Jenkins ARIMA variety that have change points in the time series at specific intervention time points. The software product autobox produced by Dave Reilly's company will take a time series like this and automatically select the number and the locations for the interventions and fit an ARIMA models with changes at the intervention points. Typically interventions are assumed to change the level of the series without changing other characteristics but they can be more complicated as may be the case for your particular series.

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What do you mean by intervention points; the series may affected by an incident and change the behavior of the time series after that point? If interventions are expected can I divide the data set into subsamples considering the intervention points and model separately? (because I need to compare the accuracy of two techniques) –  Anthony Jun 28 '12 at 4:19

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