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...and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman (1998) Forecasting: methods and applications, Wiley & Sons: New York. * Mcomp: All data from the M1 and M3 forecast competitions. Key features: * a "forecast" method and class which can be applied to Arima, StructTS, HoltWinters and other t...

...and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman (1998) Forecasting: methods and applications, Wiley & Sons: New York. * Mcomp: All data from the M1 and M3 forecast competitions. Key features: * a "forecast" method and class which can be applied to Arima, StructTS, HoltWinters and other t...

Hi all, I have some residuals from regression, and i suspect they have correlations in them... I am willing to cast the correlation into a ARMA(p, q) framework, what's the best way to identify the most suitable p, and q, and fit ARMA(p, q) model and then correct for the correlations in regression? I know there are functions in R, I have used them before, but I just want to see if I can do

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