Choose any Time Series Data (could be from the Time Series Data Library

http://www-personel.buseco.monash.edu.au/~hyndman/TSDL/ )

a) Plot the time series with the appropriatly labeled axes. In words describe the Trend, Seasonality, and Irregular components of thr time series. Plot the ACF and PACF of the time series

b) Is the time series stationary? Does it have constatnt variance?

c) If the time series does not have constant variance, suggest a transformation and plot the transformed time series or use ARCH or GARCH models

d) Plot the ACF and PACF of the transformed time series.Examine these plots for non-stationary.If appropriate difference the data and seasonality difference the data.(Note this di=etermines d and D in the ARIMA(p,d,q)s model.

e) After differencing the data plot it and the aACF and PACF.Examine these plots for the seasonality parameters P and/or Q.If appropriate apply the model and save the residuals

d) Plot the residuals and the ACF and PACF.Examine these plots for nonseasonal parameters p and/or q.If approptiated apply the model and save the residuals

f) Plot the residuals and the ACF and PACF of thr residuals.Do residuals appear to be white noise?

g) Report the formula with the estimated parameters for the model.Test each parameter for statistical significance

h) Forecast the model one season into the future using fitted model.