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I want to forecast timeseries data. I read in previous posts that module statsmodels has the required tool for using ARMA method for forecasting which is exactly the one I have been looking for. In spite of that I am having trouble in forecasting the data. Can someone explain the various parameters used in the model and/or provide a sample example?
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Autoregressive Integrated Moving Average Model. An ARIMA model is a class of statistical models for analyzing and forecasting time series data. It explicitly caters to a suite of standard structures in time series data, and as such provides a simple yet powerful method for making skillful time series forecasts.
An ARMA model is a stationary model; If your model isn't stationary, then you can achieve stationarity by taking a series of differences. The “I” in the ARIMA model stands for integrated; It is a measure of how many non-seasonal differences are needed to achieve stationarity.
The question is very general, for background information Rob Hyndman's link or any text book for time series analysis will be useful.
Skipper Seabold presented a tutorial at the scipy conference that includes an ARMA example
https://github.com/jseabold/tutorial/blob/master/tsa_arma.py
The various methods and options are described in the documentation for ARMA
http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.arima_model.ARMA.html
There is currently no book or article style description of the ARMA model in statsmodels that I'm aware of, but maybe you will get more specific answers if you ask a more specific question with what you want to do and what your troubles are.
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