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Time Series Graphics, Basic Forecasting Tools and Methods

1. 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)
2. What is your best estimate of the average temperature in June 1995?
3. Make a time plot of the data. Is there any time pattern in the temperature readings?
4. For each of the following series, what sort of time patterns would you expect to see? (6 marks)
5. Monthly retail sales of computer hard drives for the past 10 years at your local store.
6. Daily sales at a fast-food store over the last six months.
7. Weekly electricity consumption for your local area over the past 10 years.
8. 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)
9. United States GDP from global_economy.

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

1. 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)
2. Produce a time plot of the data and describe the patterns in the graph.
3. What features of the data indicate a transformation may be appropriate?
4. Transform the data using logarithms and do another time plot.
5. Calculate forecasts for the transformed data for each year from 1987 to 1994 using the seasonal naïve method.
6. Compute the RMSE, MAE, MAPE and MASE.
7. Transform your forecast for 1994 back to the original scale. Add the forecast to your graph.
8. From the graphs you have made, can you suggest a better forecasting method?
9. Consider the number of pigs slaughtered in New South Wales (dataset aus_livestock). (24 marks)
10. Produce some plots of the data in order to become familiar with it.
11. Split the data into a training set and a test set, where the test set is six years of data.
12. Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?
13. For the best method, compute the residuals and plot them. What do the plots tell you?
14. Can you invent a better forecasting method than any of the benchmark methods for these data?