1. Economics 173 Business Statistics Lecture 23 © Fall 2001, Professor J. Petry http://www.cba.uiuc.edu/jpetry/Econ_173_fa01/

2. Time-series Analysis and Forecasting Chapter 20

3. 3 20.1 Introduction Any variable that is measured over time in sequential order is called a time series. We analyze time series to detect patterns. The patterns help in forecasting future values of the time series. The time series exhibit a downward trend pattern. Future expected value

4. 4 20.2 Components of a Time Series A time series can consist of four components. –Long - term trend (T). –Cyclical effect (C). –Seasonal effect (S). –Random variation (R). A trend is a long term relatively smooth pattern or direction, that persists usually for more than one year.

5. 5 20.2 Components of a Time Series A time series can consists of four components. –Long - term trend (T). –Cyclical effect (C). –Seasonal effect (S). –Random variation (R). A cycle is a wavelike pattern describing a long term behavior (for more than one year). Cycles are seldom regular, and often appear in combination with other components. 6-88 12-88 6-89 12-89 6-90

6. 6 20.2 Components of a Time Series A time series can consists of four components. –Long - term trend (T). –Cyclical effect (C). –Seasonal effect (S). –Random variation (R). The seasonal component of the time-series exhibits a short term (less than one year) calendar repetitive behavior. 6-88 12-88 6-89 12-89 6-90

7. 7 We try to remove random variation thereby, identify the other components. 20.2 Components of a Time Series A time series can consists of four components. –Long - term trend (T). –Cyclical effect (C). –Seasonal effect (S). –Random variation (R). Random variation comprises the irregular unpredictable changes in the time series. It tends to hide the other (more predictable) components.

8. 8 –There are two commonly used time series models: –The Additive model y t = T t + C t + S t + R t –The multiplicative model y t = T t x C t x S t x R t Time-series models

9. 9 20.3 Smoothing Techniques To produce a better forecast we need to determine which components are present in a time series. To identify the components present in the time series, we need first to remove the random variation. This can be easily done by smoothing techniques.

10. 10 Moving Averages –A k-period moving average for time period t is the arithmetic average of the time series values starting at period t and counting k periods backward. –Example: A 3-period moving average for period t is calculated by (y t + y t-1 + y t-2 )/3. –In general a k-period moving average consists of the time series values in periods... t-k+1 y t-k+1 y t-2 t-2 y t-1 t-1 ytyt t

11. 11 Example 20.1 –To forecast future gasoline sales, the last four years quarterly sales were recorded. –Calculate the three-quarter and five-quarter moving average. Show the relevant graphs. –Data

12. 12 Solution –Solving by hand (39+37+61)/3= (37+61+58)/3= (39+37+61+58+18)/5= **** **** * * **** **** * * **** **** * *

13. 13 Notice how the averaging process removes some of the random variation. as well as seasonality.There is some trend component present,

14. 14 5-period moving average3-period moving average The 5-period moving average removes more variation than the 3-period moving average. Too much smoothing may eliminate patterns of interest. Here, the seasonality component is removed when using 5-period moving average. Too little smoothing leaves much of the variation, which disguises the real patterns.

15. 15 1.Below are the daily sales figures for Armani’s pizza. Compute a 3-day moving average. Week 1 Week 2 Monday3538 Tuesday4246 Wednesday5661 Thursday4652 Friday6773 Saturday5158 Sunday3942 Example

16. 16 Centered moving average –With even number of observations included in the moving average, the average is placed between the two periods in the middle. –To place the moving average in an actual time period, we need to center it. –Two consecutive moving averages are centered by taking their average, and placing it in the middle between them.

17. 17 Calculate the 4-period moving average and center it, for the data given below: Period Time seriesMoving Avg.Centerd Mov.Avg. 115 227 320 414 525 611 Example (Centered moving average) 19.0 20.25 21.5 (2.5) (3.5) 19.50 17.5 (4.5)

18. 18 1.Armani’s pizza is back. Compute a 2-day centered moving average. Week 1 Monday35 Tuesday42 Wednesday56 Thursday46 Friday67 Saturday51 Sunday39 Example