Srraaa
Forecasting Models
NMIMS
Forecasting techniques
Qualitative models time series models causal models 1.Delphi method 1.moving averages 1.regression analysis 2.Opinion poll 2.exponential smoothing 2.multiple regression 3.Historical Analogy 3.econometric models
4.Field Surveys 5.Business barometers 6.Extrapolation Technique 7.Input-Out put Analysis 8.Lead Lag Analysis 9.Sales force composites 10.Consumer Market survey
Simple Average Method
The historical data is used for extrapolating and forecasting. Either simple averages or moving averages could be used. In simple average method, for establishing the trend, the data is divided in two parts, and from the change, the trend is established. From seasonal averages, “seasonalizing indices” for seasons are calculated. For forecasting purposes, the forecast based on trend analysis is first calculated, and this forecast is then “seasonalized” by using seasonalizing factor. While using seasonal data, first data is “deseasonalized” by dividing it by the seasonalizing factor; then the forecast is made by trend analysis; and finally the forecast is “seasonalized” by multiplying by the seasonalizing factor.
In fact this procedure of “deseasonalize - forecast - seasonalize” can be used with any method of forecasting.
Moving Average Method
In moving average method, a moving average of suitable periods is used for developing the forecast for the next period. Time series methods generally require large amount of historical data to be available at hand. They are suitable for products, the demand for which is sustained, and is not prone to change due to fashions and change in public tastes.
Exponential smoothing methods
These methods are suitable for situations where the immediate past data is more relevant than the old data for making the forecast. The demand for consumer goods items, which is subject to change due to changes in public tastes and for the
NMIMS
Forecasting techniques
Qualitative models time series models causal models 1.Delphi method 1.moving averages 1.regression analysis 2.Opinion poll 2.exponential smoothing 2.multiple regression 3.Historical Analogy 3.econometric models
4.Field Surveys 5.Business barometers 6.Extrapolation Technique 7.Input-Out put Analysis 8.Lead Lag Analysis 9.Sales force composites 10.Consumer Market survey
Simple Average Method
The historical data is used for extrapolating and forecasting. Either simple averages or moving averages could be used. In simple average method, for establishing the trend, the data is divided in two parts, and from the change, the trend is established. From seasonal averages, “seasonalizing indices” for seasons are calculated. For forecasting purposes, the forecast based on trend analysis is first calculated, and this forecast is then “seasonalized” by using seasonalizing factor. While using seasonal data, first data is “deseasonalized” by dividing it by the seasonalizing factor; then the forecast is made by trend analysis; and finally the forecast is “seasonalized” by multiplying by the seasonalizing factor.
In fact this procedure of “deseasonalize - forecast - seasonalize” can be used with any method of forecasting.
Moving Average Method
In moving average method, a moving average of suitable periods is used for developing the forecast for the next period. Time series methods generally require large amount of historical data to be available at hand. They are suitable for products, the demand for which is sustained, and is not prone to change due to fashions and change in public tastes.
Exponential smoothing methods
These methods are suitable for situations where the immediate past data is more relevant than the old data for making the forecast. The demand for consumer goods items, which is subject to change due to changes in public tastes and for the