Characteristics of gold prices in an ARIMA model analysis I'm forecasting gold prices using an ARIMA model.  An ARIMA model requires a stationary, non-seasonal, linear series. However, after reading a few books, it seems that gold price data is nonstationary, seasonal, and non linear. 
Can anyone please give me some suggestions and solutions for the question? 
 A: ARIMA can handle seasonality, depending on the implementation in your software of choice. The "I" part is to take care of certain kinds of non-stationarity, though other kinds would require a de-trending pre-processing step of some sort.
EDIT: In answer to your comment, I'd recommend you look into the Box-Jenkins methodology. It's a series of steps to analyze your data to determine what preprocessing you need and what are the proper ARIMA parameters. Unfortunately, it's a bit of an art that you have to learn. If you're using a statistical package, some of them have ARIMA methods that do some of this analysis for you and make suggestions.
Taking a small step back, almost all data has to be preprocessed in some manner before use. (If nothing else, looking for errors and issues.)
Taking a large step back, I'd warn that you're not going to predict gold or stock prices accurately. A lot of people have tried very hard, inventing and using much more advanced techniques than ARIMA (which is very basic, and fairly old). I hope this is just an academic exercise to learn ARIMA, without any expectation that you're going to actually invest any money.
