1. ©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Time Series and Forecasting Chapter 16

2. 2 Time Series What is a time series? – a collection of data recorded over a period of time (weekly, monthly, quarterly) – an analysis of history, it can be used by management to make current decisions and plans based on long-term forecasting – Usually assumes past pattern to continue into the future

3. 3 Linear Trend Plot

4. 4 Linear Trend – Using the Least Squares Method Use the least squares method in Simple Linear Regression (Chapter 13) to find the best linear relationship between 2 variables Code time (t) and use it as the independent variable E.g. let t be 1 for the first year, 2 for the second, and so on (if data are annual)

5. 5 Year Sales ($ mil.) 20027 200310 20049 200511 200613 The sales of Jensen Foods, a small grocery chain located in southwest Texas, since 2002 are: Linear Trend – Using the Least Squares Method: An Example Yeart Sales ($ mil.) 200217 2003210 200439 2005411 2006513

6. 6 Linear Trend The long-term trend of many business series often approximates a straight line

7. 7 Goals Define the components of a time series Compute moving average Determine a linear trend equation Use a trend equation to forecast future time periods and to develop seasonally adjusted forecasts Determine and interpret a set of seasonal indexes Deseasonalize data using a seasonal index

8. 8 Components of a Time Series Trend – the smooth long term direction of a time series Cyclical Variation – the rise and fall of a time series over periods longer than one year Seasonal Variation – Patterns of change in a time series within a year which tends to repeat each year Irregular Variation – unpredictable

9. 9 Cyclical Variation – Sample Chart

10. 10 Seasonal Variation – Sample Chart

11. 11 The Moving Average Method Useful in smoothing time series to see its trend Basic method used in measuring seasonal fluctuation Applicable when time series follows fairly linear trend that have definite rhythmic pattern Centered on the middle value

12. 12 Moving Average Method - Example

13. 13 Three-year and Five-Year Moving Averages

14. 14 Seasonal Variation One of the components of a time series Seasonal variations are fluctuations that coincide with certain seasons and are repeated year after year Understanding seasonal fluctuations help plan for sufficient goods and materials on hand to meet varying seasonal demand Analysis of seasonal fluctuations over a period of years help in evaluating current sales

15. 15 Seasonal Index A number, usually expressed in percent, that expresses the relative value of a season with respect to the average for the year (100%) Ratio-to-moving-average method – The method most commonly used to compute the typical seasonal pattern – It eliminates the trend (T), cyclical (C), and irregular (I) components from the time series

16. 16 The table below shows the quarterly sales for Toys International for the years 2001 through 2006. The sales are reported in millions of dollars. Determine a quarterly seasonal index using the ratio-to- moving-average method. Seasonal Index – An Example

17. 17

18. 18 Seasonal Index – An Example

19. 19 Actual versus Deseasonalized Sales for Toys International Deseasonalized Sales = Sales / Seasonal Index

20. 20 Actual versus Deseasonalized Sales for Toys International – Time Series Plot using Minitab

21. 21 Seasonal Index – An Example Using Excel

22. 22 Seasonal Index – An Excel Example using Toys International Sales

23. 23 Seasonal Index – An Example Using Excel Given the deseasonalized linear equation for Toys International sales as Ŷ=8.109 + 0.0899t, generate the seasonally adjusted forecast for the each of the quarters of 2007 Quartert Ŷ (unadjusted forecast) Seasonal Index Quarterly Forecast (seasonally adjusted forecast) Winter2510.356750.7657.923 Spring2610.446660.5756.007 Summer2710.536571.14112.022 Fall2810.626481.51916.142 Ŷ = 8.109 + 0.0899(28) Ŷ X SI = 10.62648 X 1.519