Description
Time series are particularly useful to track variables such as revenues, costs, and profits over time. Time series models help evaluate performance and make predictions.
- Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components?
- The model can be additive or multiplicative. When we do use an additive model? When do we use a multiplicative model?
The following table gives the gross federal debt (in millions of dollars) for the U.S. every 5 years from 1945 to 2000:
Year |
Gross Federal Debt ($millions) |
1945 |
260,123 |
1950 |
256,853 |
1955 |
274,366 |
1960 |
290,525 |
1965 |
322,318 |
1970 |
380,921 |
1975 |
541,925 |
1980 |
909,050 |
1985 |
1,817,521 |
1990 |
3,206,564 |
1995 |
4,921,005 |
2000 |
5,686,338 |
Construct a scatter plot with this data. Do you observe a trend? If so, what type of trend do you observe?
Use Excel to fit a linear trend and an exponential trend to the data. Display the models and their respective r^2.
Interpret both models. Which model seems to be more appropriate? Why?
Prepare your graphical and written response in a minimum of 500 words.