2. Time Series Forecasting Outline: 1.Measuring forecast error 2.The multiplicative time series model 3.Naïve extrapolation 4.The mean forecast model 5.Moving average models 6.Weighted moving average models 7.Constructing a seasonal index using a centered moving average 8.Exponential smoothing

3. Forecast error Month/Year (1) Forecasted Value (2) Actual Value (3) = (2) – (1) Error July 2000$390$423$33 Aug 2000450429-21 Sept 200028930112 Forecasting Convenience Store Ice Sales

4. 3 measures of forecast error Mean absolute deviation Mean square error Root mean square error.

5. Actual Predicted Time Average Absolute Error (AAE) is given by: Where Y t is the actual value of variable that we seek to forecast and is the fitted or forecasted value of the variable.

6. Actual Predicted Time Mean Square Error (MSE) is given by: Where Y t is the actual value of variable that we seek to forecast and is the fitted or forecasted value of the variable. ßYou can think of MSE as the average forecast error. ßIf we have a perfect forecast, then MSE = 0.

7. Actual Predicted Time Root Mean Square Error (root MSE) is given by: Root MSE is a statistic that is typically is reported by forecasting software applications

8. The time path of a variable (such as monthly sales of building materials by supply stores) is produced by the interaction of 4 factors or components. These components are: 1.The trend component (T) 2.The seasonal component (S) 3.The cyclical component (C); and 4.The irregular component (I)

9. The trend component (T) Trend is the gradual, long- run (or secular) evolution of the variables that we are seeking to forecast.

10. Factors affecting the trend component of a time series Population changes Demographic changes. For example, spending for healthcare services is likely to rise due to the aging of the population. Sales of fast food are up due to the secular increase in the female labor force participation rate. Technological change. Sales of music on DVD have slumped due to Ipods. Typewriter sales have plumetted. Changes in consumer tastes and preferences.

11. Linear trends Trend = 10 – 25t Trend = -50 +.8t

12. Non-linear, increasing trend Trend = 10 +.3t +.3t 2

13. Non-linear, decreasing trend Trend = 10 -.4t -.4t 2

14. The seasonal component (S) Many series display a regular pattern of variability depending on the time of year. For example, sales of toys and scotch whiskey peak in December each year. Ice cream sales are higher in summer months than in winter months. Car sales tend typically to be strong in May and June and weaker in November and December.

15. The cyclical component (C) The time path of a series can be influenced by business cycle fluctuations. For example, we expect housing starts to decline in the contractionary phase of the business cycle. The same holds true for federal or state tax receipts The time path of spending for consumer durable goods is also shaped by cyclical forces. Spending for capital goods is likewise cyclical. The movie industry has the reputation for being “counter-cyclical”—for example, it flourished during the Depression.

16. The irregular component (I) The irregular component of the series, sometimes called white noise, is the remaining variability (relative to trend) that cannot be explained by seasonal or cyclical factors. The irregular component is an unexpected, non-recurring factor that affects the series. For example, hamburger sales plunge due to panic about E-Coli bacteria. Production of trucks slumps because of a strike at a GM parts plant in Ohio. Airline slump after 9/11. A cold snap affects July ice cream sales in upstate NY.

17. If you have a well-designed forecasting model, then forecasting errors should be mainly accounted for by irregular factors

18. The model Where: Y t is the value of the time series variable in period t (month t, quarter t, etc.) T t trend component of the series in period t S t is the seasonal component of the series in period t C t is the cylical component of the series at period t; and I t is the irregular component of the series in period t.

19. The trend component (T) is measured in the units in which the time series itself is measured. So, for example, the trend component for state revenues would be measured in dollars; whereas the trend component for steel production might be measured in tons.

20. The Problem: Forecast Sales of Home Furnishing Stores, October-December, 2007 The data: We have monthly data of sales of home furniture stores January 1992 to July 2007 (187 monthly observations). The data are expressed in millions of current dollars, not seasonally adjusted

21. tYrMo$ 1199211460 2199221453 3199231556 4199241622 5199251675 6199261759 7199271789 8199281814 9199291721 101992101839 111992111925 121992122246 """" """" 187200774803 The Data

23. OLS is a method of finding the line, or curve, of “best fit.” The trend function of best fit is the one that minimizes the squared sum of the vertical distances of the sample points (the actual monthly values of home furnishing sales) from the trend line (fitted values of monthly building materials sales). Our first step is to estimate the trend component of our series. This is accomplished using a ordinary least squares, or OLS for short.

24. Let: Y t be the actual value of furniture store sales in month t; Let Ŷ t be the trend value of furniture store sales in month t. The trend function we are seeking satisfies the following condition:

25. We estimate a linear trend function with Excel. It is displayed on the next slide.

26. t R 2 = 0.83

28. MonthIndex Jan0.8799 Feb0.8475 Mar0.9823 Apr0.9004 May0.9939 Jun1.0197 Jul0.9729 Aug1.0487 Sep1.0042 Oct0.9962 Nov1.123 Dec1.2969 If you sum the monthly values and divide by 12, you get 1.00. Later we show a simple technique for computing a seasonal index. Seasonal Index

29. Performing an in-sample forecast of home furnishing sales An in-sample forecast means we are forecasting home furshing sales for those months for which we already have data that have been used to estimate the trend, seasonal, and other components. Comparing forecasted, or fitted values of home furnishing sales with actual time series data gives us an idea of how well this performs. We will assume that the cyclical index is equal to 1 (C t = 1). This is a poor assumption since our period contains two business cycle contractions.

30. Let’s give an example how we use this model to Home furnishing sales for a particular month, say, April 1998. t = 76 for this month

33. tYr/MoTrendSeasonalCyclicalForecast 1902007/Oct4822.80.99620.9994799.669 1912007/Nov4840.421.1230.9795321.64 1922007/Dec4858.041.29690.9756142.882 Forecasting Using the Multiplicative Model