Final Project:

Find two time series data sets of interest to you. You might use the Time Series Data Library

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

to find your data. For each data set determine an appropriate ARIMA model.

1. Plot the time series with the appropriately labeled axes. In words describe the Trend, Seasonality, and Irregular components of the time series. Plot the ACF and PACF of the time series.
2. Is the time series stationary? Does it have constant variance? Constant variance?
3. If the time series does not have constant variance, suggest a transformation and plot the transformed time series.
4. Plot the ACF and PACF of the transformed time series. Examine these plots for nonstationarity. If appropriate difference the data and seasonally difference the data. (Note this determines d and D in the ARIMA(p,d,q)(P,D,Q)s model.)
5. After differencing the data plot it and the ACF and PACF. Examine these plots for the seasonality parameters P and/or Q. If appropriate apply the model and save the residuals.
6. Plot the residuals and the ACF and PACF of the residuals. Examine these plots for nonseasonal parameters p and/or q. If appropriated apply the model and save the residuals.
7. Plot the residuals and the ACF and PACF of the residuals. Do the residuals appear to be white noise?
8. Report the formula with the estimated parameters for your model. Test each parameter for statistical significance.
9. Forecast your model one season into the future using you fitted ARIMA model.