bus forecast

Business ForecastingWhy forecast?:aid in planning and controlforecasts often critical to both short- and long-term decision makingtechniques offer advantages over blind guessingBasis of forecasting here is the time series:set of time-ordered observations on a variableduring successive and equal time periodstechniques for analysis of variation in time series are called time series methods

Methods of forecastingQualitative MethodsSubjective estimates SurveyDelphiCausal MethodsChain RatioConsumption levelEnd useLeading IndicatorEconometricTime Series methods

Time Series Methods of forecasting1. Moving averages:extrapolative technique based on mean of past observationsshort range applications for inventory control, scheduling, pricingCan predict only one period ahead2. Exponential smoothing:extrapolative, uses weighted (more weight on more recent) combination of past and forecast valuesshort range applications as above

Time Series Methods of forecasting

4. Regression analysisassumes relationship between dependent variable and one or more explanatory variables (e.g. spot price)linear regression, multiple regressionshort & intermediate term forecasts for established products, production, marketing personnel, financing

Time Series Methods of forecasting3. Decomposition: assumes relationship between time and forecast variabletime series assumed to have systematic & non-systematic componentsused for both long term (new products, capital) and short term (advertising, inventory, financing, production)

Smoothing methodsTo forecast, one may take the following steps:1Choose a forecasting method based on forecasted knowledge about the observed pattern of the time series.2The forecasting method is used to develop a fitted value of the data.3Forecast error is calculated.4A decision is made about the appropriateness of the model based on the measure of forecast error.

Forecasting Using Moving Averages

Month

Observed Values

3 month Moving Avg

5 month Moving Avg

1

262.8

2

262.9

3

262.6

4

263.2

262.8

5

263.9

262.9

6

265.4

263.2

263.1

7

266.5

264.2

263.6

8

267.1

265.3

264.3

9

268.5

266.3

265.2

10

269.7

267.4

266.3

11

270.4

268.4

267.4

12

269.4

269.5

268.4

_1018255904.xls

Chart1

262.811

262.922

262.633

263.2262.76666666674

263.9262.95

265.4263.2333333333263.08

266.5264.1666666667263.6

267.1265.2666666667264.32

268.5266.3333333333265.22

269.7267.3666666667266.28

270.4268.4333333333267.44

269.4269.5333333333268.44

Observed Values

3 month Moving Avg

5 month Moving Avg

Months

Moving Averages Forecasting

Sheet1


MonthObserved Values3 month Moving Avg5 month Moving Avg

1262.8

2262.9

3262.6

4263.2262.8

5263.9262.9

6265.4263.2263.1

7266.5264.2263.6

8267.1265.3264.3

9268.5266.3265.2

10269.7267.4266.3

11270.4268.4267.4

12269.4269.5268.4

Single Parameter Exponential Smoothing

MonthObserved Values0.20.50.8

1262.8

2262.9262.8262.8262.8

3262.6262.8262.9262.9

4263.2262.8262.7262.7

5263.9262.9263.0263.1

6265.4263.1263.4263.7

7266.5263.5264.4265.1

8267.1264.1265.5266.2

9268.5264.7266.3266.9

10269.7265.5267.4268.2

11270.4266.3268.5269.4

12269.4267.1269.5270.2

Evaluating Error in Forecasts

MonthObserved Values0.2Error 0.2e(0.2)Sq.0.5Error 0.5e(0.5)Sq.0.8Error 0.8e(0.8)Sq.

1262.8

2262.9262.80.10.01262.80.10.01262.80.10.01

3262.6262.8-0.20.04262.9-0.30.09262.9-0.30.09

4263.2262.80.40.16262.80.40.16262.70.50.25

5263.9262.911263.00.90.81263.10.80.64

6265.4263.12.35.29263.51.93.61263.71.72.89

7266.5263.62.98.41264.524265.11.41.96

8267.1264.22.98.41265.51.62.56266.20.90.81

9268.5264.83.713.69266.32.24.84266.91.62.56

10269.7265.54.217.64267.42.35.29268.21.52.25

11270.4266.34.116.81268.61.83.24269.411

12269.4267.12.35.29269.5-0.10.01270.2-0.80.64

76.7524.6213.1

MSE6.97727272732.23818181821.1909090909

Page &P of &N

&F

Sheet1

Page &P of &N

&R&8&F

Observed Values

3 month Moving Avg

5 month Moving Avg

Months


Sheet2

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

Observed Values

0.2

0.5

0.8

Months


Sheet3


Month

Observed Values

3 month Moving Avg

5 month Moving Avg

1

262.8

2

262.9

3

262.6

4

263.2

262.8

5

263.9

262.9

6

265.4

263.2

263.1

7

266.5

264.2

263.6

8

267.1

265.3

264.3

9

268.5

266.3

265.2

10

269.7

267.4

266.3

11

270.4

268.4

267.4

12

269.4

269.5

268.4

EMBED Excel.Sheet.8

_1018255904.xls

Chart1

262.811

262.922

262.633

263.2262.76666666674

263.9262.95

265.4263.2333333333263.08

266.5264.1666666667263.6

267.1265.2666666667264.32

268.5266.3333333333265.22

269.7267.3666666667266.28

270.4268.4333333333267.44

269.4269.5333333333268.44

Observed Values

3 month Moving Avg

5 month Moving Avg

Months


Sheet1



1262.8

2262.9

3262.6

4263.2262.8

5263.9262.9

6265.4263.2263.1

7266.5264.2263.6

8267.1265.3264.3

9268.5266.3265.2

10269.7267.4266.3

11270.4268.4267.4

12269.4269.5268.4



1262.8

2262.9262.8262.8262.8

3262.6262.8262.9262.9

4263.2262.8262.7262.7

5263.9262.9263.0263.1

6265.4263.1263.4263.7

7266.5263.5264.4265.1

8267.1264.1265.5266.2

9268.5264.7266.3266.9

10269.7265.5267.4268.2

11270.4266.3268.5269.4

12269.4267.1269.5270.2



1262.8

2262.9262.80.10.01262.80.10.01262.80.10.01

3262.6262.8-0.20.04262.9-0.30.09262.9-0.30.09

4263.2262.80.40.16262.80.40.16262.70.50.25

5263.9262.911263.00.90.81263.10.80.64

6265.4263.12.35.29263.51.93.61263.71.72.89

7266.5263.62.98.41264.524265.11.41.96

8267.1264.22.98.41265.51.62.56266.20.90.81

9268.5264.83.713.69266.32.24.84266.91.62.56

10269.7265.54.217.64267.42.35.29268.21.52.25

11270.4266.34.116.81268.61.83.24269.411

12269.4267.12.35.29269.5-0.10.01270.2-0.80.64

76.7524.6213.1

MSE6.97727272732.23818181821.1909090909

Page &P of &N

&F

Sheet1

Page &P of &N

&R&8&F

Observed Values

3 month Moving Avg

5 month Moving Avg

Months


Sheet2

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

Observed Values

0.2

0.5

0.8

Months


Sheet3

values


Month

Observed Values

0.2

0.5

0.8

1

262.8

2

262.9

262.8

262.8

262.8

3

262.6

262.8

262.9

262.9

4

263.2

262.8

262.7

262.7

5

263.9

262.9

263.0

263.1

6

265.4

263.1

263.4

263.7

7

266.5

263.5

264.4

265.1

8

267.1

264.1

265.5

266.2

9

268.5

264.7

266.3

266.9

10

269.7

265.5

267.4

268.2

11

270.4

266.3

268.5

269.4

12

269.4

267.1

269.5

270.2

values

Chart5

262.8111

262.9262.8262.8262.8

262.6262.82262.85262.88

263.2262.776262.725262.656

263.9262.8608262.9625263.0912

265.4263.06864263.43125263.73824

266.5263.534912264.415625265.067648

267.1264.1279296265.4578125266.2135296

268.5264.72234368266.27890625266.92270592

269.7265.477874944267.389453125268.184541184

270.4266.3222999552268.5447265625269.3969082368

269.4267.1378399642269.4723632812270.1993816474

Observed Values

0.2

0.5

0.8

Months


Sheet1



1262.8

2262.9

3262.6

4263.2262.8

5263.9262.9

6265.4263.2263.1

7266.5264.2263.6

8267.1265.3264.3

9268.5266.3265.2

10269.7267.4266.3

11270.4268.4267.4

12269.4269.5268.4



1262.8

2262.9262.8262.8262.8

3262.6262.8262.9262.9

4263.2262.8262.7262.7

5263.9262.9263.0263.1

6265.4263.1263.4263.7

7266.5263.5264.4265.1

8267.1264.1265.5266.2

9268.5264.7266.3266.9

10269.7265.5267.4268.2

11270.4266.3268.5269.4

12269.4267.1269.5270.2



1262.8

2262.9262.80.10.01262.80.10.01262.80.10.01

3262.6262.8-0.20.04262.9-0.30.09262.9-0.30.09

4263.2262.80.40.16262.80.40.16262.70.50.25

5263.9262.911263.00.90.81263.10.80.64

6265.4263.12.35.29263.51.93.61263.71.72.89

7266.5263.62.98.41264.524265.11.41.96

8267.1264.22.98.41265.51.62.56266.20.90.81

9268.5264.83.713.69266.32.24.84266.91.62.56

10269.7265.54.217.64267.42.35.29268.21.52.25

11270.4266.34.116.81268.61.83.24269.411

12269.4267.12.35.29269.5-0.10.01270.2-0.80.64

76.7524.6213.1

MSE6.97727272732.23818181821.1909090909

Page &P of &N

&F

Sheet1

Page &P of &N

&R&8&F

Observed Values

3 month Moving Avg

5 month Moving Avg

Months


Sheet2

Observed Values

0.2

0.5

0.8

Months


Sheet3

Sales Data For 28 Quarters

1991Q12891993Q12931995Q13471997Q14441991Q24101993Q24411995Q25201997Q25921991Q33011993Q34111995Q35401997Q35711991Q42131993Q43631995Q45211997Q45071992Q12121994Q13241996Q13811992Q23711994Q24621996Q25941992Q33741994Q33791996Q35731992Q43331994Q43011996Q4504

Chart3

359.2458383372

349.3987609961

279.3386836813

235.062305828

263.5298191262

316.1632690965

347.0852747402

367.4917739002

364.2180990754

375.8167160958

381.4225880166

400.5991409182

402.7531197967

393.7127501956

351.72545221

332.1772490809

431.3436190415

443.1398919951

501.1391667372

574.9646072132

473.6078353165

506.2021073944

531.7643380378

556.203765903

551.9209419436

504.4977231944

529.9082670498

559.5145026048

Sheet1

YEARQUARTERSalesMAAv MATrend

TCSRTCSRSTCRTCRCR

1991Q102890.80359.25274.961.31

1991Q214101.17349.40285.501.22

1991Q32301303.25293.631.0251.08279.34296.040.941.160.81

1991Q43213284.00279.130.7630.91235.06306.590.770.980.78

1992Q14212274.25283.380.7480.80263.53317.130.830.850.98

1992Q25371292.50307.501.2071.17316.16327.670.960.851.13

1992Q36374322.50332.631.1241.08347.09338.211.030.941.09

1992Q47333342.75351.500.9470.91367.49348.761.051.011.04

1993Q18293360.25364.880.8030.80364.22359.301.011.030.98

1993Q29441369.50373.251.1821.17375.82369.841.021.030.99

1993Q310411377.00380.881.0791.08381.42380.381.001.010.99

1993Q411363384.75387.380.9370.91400.60390.931.021.011.01

1994Q112324390.00386.000.8390.80402.75401.471.001.010.99

1994Q213462382.00374.251.2341.17393.71412.010.960.990.96

1994Q314379366.50369.381.0261.08351.73422.560.830.930.89

1994Q415301372.25379.500.7930.91332.18433.100.770.850.90

1995Q116347386.75406.880.8530.80431.34443.640.970.861.13

1995Q217520427.00454.501.1441.17443.14454.180.980.901.08

1995Q318540482.00486.251.1111.08501.14464.731.081.011.07

1995Q419521490.50499.751.0430.91574.96475.271.211.091.11

1996Q120381509.00513.130.7430.80473.61485.810.971.090.90

1996Q221594517.25515.131.1531.17506.20496.351.021.070.95

1996Q322573513.00520.881.1001.08531.76506.901.051.011.03

1996Q423504528.75528.500.9540.91556.20517.441.071.051.03

1997Q124444528.25528.000.8410.80551.92527.981.051.060.99

1997Q225592527.75528.131.1211.17504.50538.530.941.020.92

1997Q326571528.501.08529.91549.070.970.980.98

1997Q4275070.91559.51559.611.000.971.03

Q1Q2Q3Q4

0.801.171.080.913.9615951117

Sheet1

Sheet2

1991Q12891993Q12931995Q13471997Q1444

1991Q24101993Q24411995Q25201997Q2592

1991Q33011993Q34111995Q35401997Q3571

1991Q42131993Q43631995Q45211997Q4507

1992Q12121994Q13241996Q1381

1992Q23711994Q24621996Q2594

1992Q33741994Q33791996Q3573

1992Q43331994Q43011996Q4504

Sheet3

Decomposition of Time Series Components DEMAND Y = T*C*S*RTREND (T)The long term growth / decline in demandBUSINESS CYCLE (C)Deviation from trend because of environmental factorsSEASONAL COMPONENT (S)Annually repetitive demand fluctuationsRANDOM COMPONENT ( R)Irregular, unpredictable residual component

Sales Forecast for 19981998 Q1 ( 29)Extrapolating the given data values givesForecast for Q1 as 570.15.Forecast for Q2 as 580.7

This doesnt take care of the seasonal effect and the business cycle impact

Chart5

1.1579809554

0.9780338502

0.8470920292

0.8541936716

0.9407004915

1.014944631

1.0312146601

1.0278561227

1.0108588756

1.0145422974

1.0102225491

0.9945075436

0.9303862313

0.8516471934

0.8572128706

0.9049820233

1.0087734555

1.0879353255

1.0876669195

1.0681624265

1.0145927073

1.0479378295

1.0564375778

1.0190227998

0.9824194963

0.9672490574

Cyclical Component

Sheet1

YEARQUARTERSalesMAAv MATrend

TCSRTCSRSTCRTCRCR

1991Q102890.80359.25274.961.31

1991Q214101.17349.40285.501.22

1991Q32301303.25293.631.0251.08279.34296.040.941.160.81

1991Q43213284.00279.130.7630.91235.06306.590.770.980.78

1992Q14212274.25283.380.7480.80263.53317.130.830.850.98

1992Q25371292.50307.501.2071.17316.16327.670.960.851.13

1992Q36374322.50332.631.1241.08347.09338.211.030.941.09

1992Q47333342.75351.500.9470.91367.49348.761.051.011.04

1993Q18293360.25364.880.8030.80364.22359.301.011.030.98

1993Q29441369.50373.251.1821.17375.82369.841.021.030.99

1993Q310411377.00380.881.0791.08381.42380.381.001.010.99

1993Q411363384.75387.380.9370.91400.60390.931.021.011.01

1994Q112324390.00386.000.8390.80402.75401.471.001.010.99

1994Q213462382.00374.251.2341.17393.71412.010.960.990.96

1994Q314379366.50369.381.0261.08351.73422.560.830.930.89

1994Q415301372.25379.500.7930.91332.18433.100.770.850.90

1995Q116347386.75406.880.8530.80431.34443.640.970.861.13

1995Q217520427.00454.501.1441.17443.14454.180.980.901.08

1995Q318540482.00486.251.1111.08501.14464.731.081.011.07

1995Q419521490.50499.751.0430.91574.96475.271.211.091.11

1996Q120381509.00513.130.7430.80473.61485.810.971.090.90

1996Q221594517.25515.131.1531.17506.20496.351.021.070.95

1996Q322573513.00520.881.1001.08531.76506.901.051.011.03

1996Q423504528.75528.500.9540.91556.20517.441.071.051.03

1997Q124444528.25528.000.8410.80551.92527.981.051.060.99

1997Q225592527.75528.131.1211.17504.50538.530.941.020.92

1997Q326571528.501.08529.91549.070.970.980.98

1997Q4275070.91559.51559.611.000.971.03

1998Q128570.15

1998Q229580.70

1998Q330591.24

1998Q431601.78

Q1Q2Q3Q4

0.801.171.080.913.9615951117

Sheet1

Sheet2

Cyclical Component

Sheet3

1991Q12891993Q12931995Q13471997Q1444

1991Q24101993Q24411995Q25201997Q2592

1991Q33011993Q34111995Q35401997Q3571

1991Q42131993Q43631995Q45211997Q4507

1992Q12121994Q13241996Q1381

1992Q23711994Q24621996Q2594

1992Q33741994Q33791996Q3573

1992Q43331994Q43011996Q4504

Sales Forecast for 1998Q1(29)T = 570.15, C = 0.95, S = 0.8TCS = 433.3 Q2 (30)T = 580.7, C = 0.94, S = 1.17TCS = 638.65

Evaluating the Error in Forecasting

et = Xt Ft

Evaluating the Error in ForecastingTracking Signal = e / MAD

Ideal Value is 0Large + value indicate pessimistic approachLarge - value indicate optimistic approach

Comments on smoothing constant Small values reduce impact of remote values slowly, build in slow response to changesLarge values dampen remote values quickly, can cause forecast to over-respond to irregular movementsGeneral principle: values between 0.05 and 0.30 work well for exponential smoothingValues > 0.3 suggest another method better

Forecasting SummaryEmpirical studies show that the accuracy of Qualitative methods is much worse compared to Quantitative methods Too much mathematical focus will limit the accuracy of Quantitative methods alsoCombining forecasts produce better resultsTime horizon is very crucial Study among 150 fortune 500 companies (1990)Forecasting methoduse %% who are satisfiedMoving Average8558Trend line8232Jury method8154Regression7267

Copper prices for 3 yrs

Copper prices for 10 years

Copper prices for 100 years

bus forecast

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