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**Time Series Graphics, Basic Forecasting Tools and Methods **

Please explain your answers to receive full marks.

- The data set
**paris**(in**fpp2**package) gives the average monthly temperatures, in Celsius degrees, in Paris between January 1994 and May 1995. (**8 marks**) - What is your best estimate of the average temperature in June 1995?
- Make a time plot of the data. Is there any time pattern in the temperature readings?
- For each of the following series, what sort of time patterns would you expect to see? (
**6 marks**) - Monthly retail sales of computer hard drives for the past 10 years at your local store.
- Daily sales at a fast-food store over the last six months.
- Weekly electricity consumption for your local area over the past 10 years.
- For each of the following series, make a graph of the data, describe the main features and, if transforming seems appropriate, do so and describe the effect. (
**18 marks**) - United States GDP from
**global_economy**.

**ADM 4307 Business Forecasting Analytics**

- Slaughter of Victorian “Bulls, bullocks and steers” in
**aus_livestock**. - Gas production from
**aus_production**. - In the following graphs, four time series are plotted along with their ACFs. Which ACF goes with which time series? (
**8 marks**)

- The data set
**souvenirs**concerns the monthly sales figures of a shop which opened in January 1987 and sells gifts, souvenirs, and novelties. The shop is situated on the wharf at a beach resort town in Queensland, Australia. (**26 marks**) - Produce a time plot of the data and describe the patterns in the graph.
- What features of the data indicate a transformation may be appropriate?
- Transform the data using logarithms and do another time plot.
- Calculate forecasts for the transformed data for each year from 1987 to 1994 using the seasonal naïve method.
- Compute the RMSE, MAE, MAPE and MASE.
- Transform your forecast for 1994 back to the original scale. Add the forecast to your graph.
- From the graphs you have made, can you suggest a better forecasting method?
- Consider the number of pigs slaughtered in New South Wales (dataset
**aus_livestock**). (**24 marks**) - Produce some plots of the data in order to become familiar with it.
- Split the data into a training set and a test set, where the test set is six years of data.
- Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?
- For the best method, compute the residuals and plot them. What do the plots tell you?
- Can you invent a better forecasting method than any of the benchmark methods for these data?