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Steps Of ForecastingGroup 2
step of forecasting
step of forecasting
Determine the use of the forecastWho needs the forecast?All organizations operate in the atmosphere of uncertainty.Decisions to be made affects future of the organization.
Select the items to be forecastedThe item to be forecasted. Dependent variable to be studied.
Determine the time horizon of the forecastShort-range forecastUp to 1 yearPurchasing, job scheduling, job assignmentsMedium-range forecast1 year to 3 yearsSales and production planningLong-range forecast3+ yearsNew product planning, research and development
Select Forecasting approachQualitative MethodsUsed when situation is vague and little data existNew productsNew technologyInvolves intuition, experience
Quantitative MethodsUsed when situation is ‘stable’ and historical data existExisting productsCurrent technologyInvolves mathematical techniques
Data collection One of the most difficult and time consuming part of forecasting is the collection of valid and reliable data. Forecast can be no more accurate than the data on which it is basedData can be collected from- primary source and secondary source
Four criteria can be applied to the determination of whether the data will be useful-Data should be reliable and accurateData should be relevantData should be consistentData should be timely
Data ReductionSince available data can be either too much or too less, data reduction is necessary.Decide which data is most complete, valid and reliable to increase data accuracy.Some times accurate data may be available but only in certain historic periods.
Exploring Time Series Data PatternsHorizontal pattern- When data observation fluctuate around a constant level or meanTrend pattern- When data observation grow or decline over an extended period of time Cyclic pattern- When data observation exhibits rises and falls that are not of a fixed periodSeasonal Pattern- When data observation are influenced by  seasonal factors.
Exploring Data Patterns with Auto correlation AnalysisAutocorrelation is the correlation between a variable lagged one or more period itself.It is used to detect non randomness of dataTo identify an appropriate time series model if data is not random
step of forecasting
step of forecasting
Y= 1704/12 = 142r1 = 843/1474 = .572
Select the forecasting model(s)The most prominently used models are:Exponential smoothing method with  1 or 2 variables.Regression ModelsOnce the model has been judicially selected, its parameters are estimated for model fitting purposes.
Make the forecastForecast is made for a particular period.
Forecast evaluationComparing Forecast value with actual historical values.……………………… ^Error : et = yt –y t
Thank you

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step of forecasting

  • 4. Determine the use of the forecastWho needs the forecast?All organizations operate in the atmosphere of uncertainty.Decisions to be made affects future of the organization.
  • 5. Select the items to be forecastedThe item to be forecasted. Dependent variable to be studied.
  • 6. Determine the time horizon of the forecastShort-range forecastUp to 1 yearPurchasing, job scheduling, job assignmentsMedium-range forecast1 year to 3 yearsSales and production planningLong-range forecast3+ yearsNew product planning, research and development
  • 7. Select Forecasting approachQualitative MethodsUsed when situation is vague and little data existNew productsNew technologyInvolves intuition, experience
  • 8. Quantitative MethodsUsed when situation is ‘stable’ and historical data existExisting productsCurrent technologyInvolves mathematical techniques
  • 9. Data collection One of the most difficult and time consuming part of forecasting is the collection of valid and reliable data. Forecast can be no more accurate than the data on which it is basedData can be collected from- primary source and secondary source
  • 10. Four criteria can be applied to the determination of whether the data will be useful-Data should be reliable and accurateData should be relevantData should be consistentData should be timely
  • 11. Data ReductionSince available data can be either too much or too less, data reduction is necessary.Decide which data is most complete, valid and reliable to increase data accuracy.Some times accurate data may be available but only in certain historic periods.
  • 12. Exploring Time Series Data PatternsHorizontal pattern- When data observation fluctuate around a constant level or meanTrend pattern- When data observation grow or decline over an extended period of time Cyclic pattern- When data observation exhibits rises and falls that are not of a fixed periodSeasonal Pattern- When data observation are influenced by seasonal factors.
  • 13. Exploring Data Patterns with Auto correlation AnalysisAutocorrelation is the correlation between a variable lagged one or more period itself.It is used to detect non randomness of dataTo identify an appropriate time series model if data is not random
  • 16. Y= 1704/12 = 142r1 = 843/1474 = .572
  • 17. Select the forecasting model(s)The most prominently used models are:Exponential smoothing method with 1 or 2 variables.Regression ModelsOnce the model has been judicially selected, its parameters are estimated for model fitting purposes.
  • 18. Make the forecastForecast is made for a particular period.
  • 19. Forecast evaluationComparing Forecast value with actual historical values.……………………… ^Error : et = yt –y t

Editor's Notes

  • #5: Decisions made by using forecasting technique are more accurate than those made on the basis of “gut” feelings.
  • #20: Most forecast go wrong bcos it is futuristic. Underestimation, oversestimation