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- 1. Time Series, Forecasting, and Index Numbers Slide 1 Shakeel Nouman M.Phil Statistics Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 2. 12 Slide 2 Time Series, Forecasting, and Index Numbers • Using Statistics • Trend Analysis • Seasonality and Cyclical Behavior • The Ratio-to-Moving-Average Method • Exponential Smoothing Methods • Index Numbers • Summary and Review of Terms Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 3. 12-1 Using Statistics Slide 3 A time series is a set of measurements of a variable that are ordered through time. Time series analysis attempts to detect and understand regularity in the fluctuation of data over time. Regular movement of time series data may result from a tendency to increase or decrease through time - trend- or from a tendency to follow some cyclical pattern through time - seasonality or cyclical variation. Forecasting is the extrapolation of series values beyond the region of the estimation data. Regular variation of a time series can be forecast, while random variation cannot. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 4. The Random Walk Slide 4 A random walk: Zt- Zt-1=at or equivalently: Zt= Zt-1+at The difference between Z in time t and time t-1 is a random error. R andom W alk 15 10 5 Z 0 There is no evident regularity in a random walk, since the difference in the series from period to period is a random error. A random walk is not forecastable. a 0 10 20 30 40 50 t Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 5. A Time Series as a Superposition of Cyclical Functions Slide 5 A Time Series as a Superposition of Two Wave Functions and a Random Error (not shown) z 4 3 2 1 0 -1 0 10 20 30 40 50 t In contrast with a random walk, this series exhibits obvious cyclical fluctuation. The underlying cyclical series - the regular elements of the overall fluctuation - may be analyzable and forecastable. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 6. 12-2 Trend Analysis: Example 12-1 Slide 6 The following output was obtained using the template. Zt = 696.89 + 109.19t Note: The template contains forecasts for t = 9 to t = 20 which corresponds to years 2002 to 2013. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 7. 12-2 Trend Analysis: Example 12-1 Slide 7 Straight line trend. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 8. Example 12-2 Slide 8 The forecast for t = 10 (year 2002) is 345.27 Observe that the forecast model is Zt = 82.96 + 26.23t Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 9. 12-3 Seasonality and Cyclical Behavior G ro s s E arning s : A nn ual Monthly S ale s of S untan Oil Monthly Numbers of Airline P as s engers 20 100 11000 P a s s e ng er s E a rnin g s S a le s 200 Slide 9 15 10 10000 9000 8000 7000 0 6000 5 J FMAMJ J AS OND J FMAMJ J ASOND J FMAMJ J A 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 Time t 86 87 88 89 90 91 92 93 94 95 Ye ar When a cyclical pattern has a period of one year, it is usually called seasonal variation. A pattern with a period of other than one year is called cyclical variation. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 10. Time Series Decomposition • • • Slide 10 Types of Variation Trend (T) Seasonal (S) Cyclical (C) Random or Irregular (I) Additive Model Zt = Tt + St + Ct + It Multiplicative Model Zt = (Tt )(St )(Ct )(It) Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 11. Estimating an Additive Model with Seasonality Slide 11 An additive regression model with seasonality: Zt=0+ 1 t+ 2 Q1+ 3 Q2 + 4 Q3 + at where Q1=1 if the observation is in the first quarter, and 0 otherwise Q2=1 if the observation is in the second quarter, and 0 otherwise Q3=1 if the observation is in the third quarter, and 0 otherwise Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 12. 12-4 The Ratio-to-MovingAverage Method Slide 12 A moving average of a time series is an average of a fixed number of observations that moves as we progress down the series. Time, t: Series, Zt: 1 15 2 12 3 11 4 18 6 7 8 9 10 11 12 13 16 14 17 20 18 21 16 14 Five-period 15.4 15.6 16.0 17.2 17.6 17.0 18.0 18.4 17.8 17.6 14 19 3 11 4 18 14 19 moving average: Time, t: Series, Zt: 1 15 2 12 5 21 5 6 7 8 9 10 11 12 21 16 14 17 20 18 21 16 (15 + 12 + 11 + 18 + 21)/5=15.4 (12 + 11 + 18 + 21 + 16)/5=15.6 (11 + 18 + 21 + 16 + 14)/5=16.0 13 14 . . . . . (18 + 21 + 16 + 14 + 19)/5=17.6 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 13. Comparing Original Data and Smoothed Moving Average Original Series and Five-Period Moving Averages • Slide 13 Moving Average: – “Smoother” – Shorter – Deseasonalized 20 Z – Removes seasonal and irregular components 15 – Leaves trend and cyclical components 10 0 5 10 15 t Z t TSCI SI MA TC Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 14. Ratio-to-Moving Average • Slide 14 Ratio-to-Moving Average for Quarterly Data Compute a four-quarter moving-average series. Center the moving averages by averaging every consecutive pair and placing the average between quarters. Divide the original series by the corresponding moving average. Then multiply by 100. Derive quarterly indexes by averaging all data points corresponding to each quarter. Multiply each by 400 and divide by sum. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 15. Ratio-to-Moving Average: Example 12-3 Ratio to Moving Average * * 93.3 100.9 110.2 93.8 90.1 107.5 106.4 100.7 90.5 95.0 113.9 97.9 * * Four-Quarter Moving Averages Actual Smoothed Actual Smoothed 170 160 Sales Quarter 1998W 1998S 1998S 1998F 1999W 1999S 1999S 1999F 2000W 2000S 2000S 2000F 2002W 2002S 2002S 2002F Simple Centered Moving Moving Sales Average Average 170 * * 148 * * 141 * 151.125 150 152.25 148.625 161 150.00 146.125 137 147.25 146.000 132 145.00 146.500 158 147.00 147.000 157 146.00 147.500 145 148.00 144.000 128 147.00 141.375 134 141.00 141.000 160 141.75 140.500 139 140.25 142.000 130 140.75 * 144 143.25 * Slide 15 150 Moving Average Length: 4 140 MAPE: MAD: MSD: 130 0 5 10 7.816 10.955 152.574 15 T ime Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 16. Seasonal Indexes: Example 12-3 Year Winter 1998 1999 2000 Quarter Spring Summer 110.2 106.4 2002 Sum Average 93.8 100.7 113.9 93.3 90.1 90.5 97.9 330.5 110.17 292.4 97.47 273.9 91.3 Slide 16 Fall 100.9 107.5 95.0 303.4 101.13 Sum of Averages = 400.07 Seasonal Index = (Average)(400)/400.07 Index 110.15 Seasonal 97.45 91.28 101.11 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 17. Deseasonalized Series: Example 12-3 Original and SeasonallyAdjusted Series 170 160 Sales Seasonal Deseasonalized Quarter Sales Index (S) Series(Z/S)*100 1998W 170 110.15 154.34 1998S 148 97.45 151.87 1998S 141 91.28 154.47 1998F 150 101.11 148.35 1999W 161 110.15 146.16 1999S 137 97.45 140.58 1999S 132 91.28 144.51 1999F 158 101.11 156.27 2000W 157 110.15 142.53 2000S 145 97.45 148.79 2000S 128 91.28 140.23 2000F 134 101.11 132.53 2002W 160 110.15 145.26 2002S 139 97.45 142.64 2002S 130 91.28 142.42 2002F 144 101.11 142.42 Slide 17 150 140 130 1992W 1992S 1992S 1992F t Original Deseasonalized - - - Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 18. The Cyclical Component: Example 12-3 Trend Line and Moving Averages 180 Sales 170 160 150 140 130 120 1992W 1992S 1992S t 1992F Slide 18 The cyclical component is the remainder after the moving averages have been detrended. In this example, a comparison of the moving averages and the estimated regression line: Z 155.275 11059 t . illustrates that the cyclical component in this series is negligible. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 19. Example 12-3 using the Template Slide 19 The centered moving average, ratio to moving average, seasonal index, and deseasonalized values were determined using the Ratio-to-Moving-Average method. This displays just a partial output for the quarterly forecasts. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 20. Example 12-3 using the Template Slide 20 Graph of the quarterly Seasonal Index Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 21. Example 12-3 using the Template Slide 21 Graph of the Data and the quarterly Forecasted values Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 22. Example 12-3 using the Template Slide 22 The centered moving average, ratio to moving average, seasonal index, and deseasonalized values were determined using the Ratio-to-Moving-Average method. This displays just a partial output for the monthly forecasts. Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 23. Example 12-3 using the Template Slide 23 Graph of the monthly Seasonal Index Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 24. Example 12-3 using the Template Slide 24 Graph of the Data and the monthly Forecasted values Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 25. Forecasting a Multiplicative Series: Example 12-3 Slide 25 The forecast of a multiplicative series : ˆ Z = TSC Forecast for Winter 2002 (t = 17) : Trend : z = 152.26 - (0.837)(17) = 138.03 ˆ S = 1.1015 C 1 (negligibl e) ˆ Z = TSC = (1)(138.03)(1.1015) = 152.02 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 26. Multiplicative Series: Review Slide 26 Z ( Trend )( Seasonal ( Cyclical )( Irregular ) TSCI MA ( Trend )( Cyclical ) TC Z TSCI SI MA TC S = Average of SI (Ratio - to - Moving Averages) Z TSCI CTI (Deseasonalized Data) S S Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 27. 12-5 Exponential Smoothing Methods Slide 27 Smoothing is used to forecast a series by first removing sharp variation, as does the moving average. Weights Decline as we go back in Time Weights Decline as We Go Back in Time and Sum to 1 W e ig ht Weight 0.4 0.3 0.2 0.1 0.0 -15 -10 -5 Exponential smoothing is a forecasting method in which the forecast is based in a weighted average of current and past series values. The largest weight is given to the present observations, less weight to the immediately preceding observation, even less weight to the observation before that, and so on. The weights decline geometrically as we go back in time. 0 Lag -10 Lag 0 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 28. The Exponential Smoothing Model Slide 28 Given a weighting factor: 0 < w < 1: 2 3 Z t 1 w ( Z t ) w (1 w )( Z t 1 ) w (1 w ) ( Z t 2 ) w (1 w ) ( Z t 3 ) Since 2 3 Z t w ( Z t 1 ) w (1 w )( Z t 2 ) w (1 w ) ( Z t 3 ) w (1 w ) ( Z t 4 ) 2 3 (1 w ) Z t w (1 w )( Z t 1 ) w (1 w ) ( Z t 2 ) w (1 w ) ( Z t 3 ) So Z t 1 w(Z t ) (1 w)(Z t ) Z t 1 Z t (1 w)(Z t - Z t ) Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 29. Example 12-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Z 925 940 924 925 912 908 910 912 915 924 943 962 960 958 955 * w=.4 w=.8 925.000 925.000 931.000 928.200 926.920 920.952 915.771 913.463 912.878 913.727 917.836 927.902 941.541 948.925 952.555 953.533 925.000 925.000 937.000 926.600 925.320 914.664 909.333 909.867 911.573 914.315 922.063 938.813 957.363 959.473 958.295 955.659 Exp o n e ntial S m o othing: w=0 .4 and w=0 .8 960 950 w = .4 Day Slide 29 940 930 920 910 0 5 10 15 Day Original data: Smoothed, w=0.4: ...... Smoothed, w=0.8: ----- Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 30. Example 12-4 – Using the Template Slide 30 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 31. 12-6 Index Numbers Slide 31 An index number is a number that measures the relative change in a set of measurements over time. For example: the Dow Jones Industrial Average (DJIA), the Consumer Price Index (CPI), the New York Stock Exchange (NYSE) Index. Value in period i Index number in period i: = 100 Value in base period Changing the base period of an index: Old index value New index value: = 100 Index value of new base Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 32. Index Numbers: Example 12-5 Pric e and Index (1982=100) of Natural Gas Price Index Index Year Price 1984-Base 1991-Base 121 121 133 146 162 164 172 187 197 224 255 247 238 222 100.0 100.0 109.9 120.7 133.9 135.5 142.1 154.5 162.8 185.1 210.7 204.1 196.7 183.5 64.7 64.7 71.1 78.1 86.6 87.7 92.0 100.0 105.3 119.8 136.4 132.1 127.3 118.7 250 Original P ric e 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Slide 32 Index (1984) 150 Index (1991) 50 1985 1990 1995 Ye ar Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 33. Consumer Price Index – Example 12-6 Consumer Price index (CPI): 1967=100 Slide 33 Example 12-6: 450 CPI 350 250 150 50 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 Ye a r Salary Adjusted Salary = 100 CPI Adjusted Year Salary Salary 1980 29500 11953.0 1981 31000 11380.3 1982 33600 11610.2 1983 35000 11729.2 1984 36700 11796.8 1985 38000 11793.9 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 34. Example 12-6: Using the Template Slide 34 Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer
- 35. Slide 35 Name Religion Domicile Contact # E.Mail M.Phil (Statistics) Shakeel Nouman Christian Punjab (Lahore) 0332-4462527. 0321-9898767 sn_gcu@yahoo.com sn_gcu@hotmail.com GC University, . (Degree awarded by GC University) M.Sc (Statistics) Statitical Officer (BS-17) (Economics & Marketing Division) GC University, . (Degree awarded by GC University) Livestock Production Research Institute Bahadurnagar (Okara), Livestock & Dairy Development Department, Govt. of Punjab Time Series, Forecasting, and Index Numbers By Shakeel Nouman M.Phil Statistics Govt. College University Lahore, Statistical Officer

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