1. Forecasts and Projections “A trend is a trend is a trend, But the question is, will it bend? Will it alter its course Through some unforeseen force And come to a premature end?” Cairncross (1969)

2. Forecasting is a method of predicting a future trend in a data series. Involves making a Judgement What impact do other areas have upon the variable we are predicting? Forecasts v’s projections Predicting next data point or series based on previous data series Can use 1, 2, 3 or more series of data to predict next series of data

3. Why do we use projections? What do you use projections for?

4. 1.Looking at our data 2.Which model do we use? 3.How far can we project our data? 4.Things to think about? Getting started with projections

5. The data Source:

6. 1.How much data do we have? Is it enough? 2.Time series – are there any gaps in the data? If so where are these gaps? What might have caused them? 3.Is the data comparable? Have there been any changes in how the data was measured? 4.Are there any outliers? If so what could have caused them? Data suitability

7. Models of best fit Linear ModelExponential Model Polynomial Model Order 3 Models of best fit Polynomial Model Order 2

8. Linear Source:

9. Exponential

10. Polynomial Order 2

11. Polynomial Order 3

12. Linear Source:

13. Exponential

14. Polynomial Order 2

15. Polynomial Order 3

16. In the future - Polynomial Order 3

17. How far forward can we project our data? Do you need a long term or short term projection? How much data do you have? How frequent is the data? Are there any other factors we need to consider?

18. Long Term V’s Short Term Projections Long term Can counter act seasonal variations Can minimise fluctuations and isolated events in the data Accuracy decreases the further away from original data we get Short term Over emphasises seasonal variations – can we counteract another way? Isolated events in data carry more weight Closer to original data therefore more accurate

19. Forecast or projection? How and why do we use projections? The data suitability – how much data, what the data looks like. Which Model to use? Long or short term projection? It’s all about the data Summary