1 / 20

Forecasting Part 3

Forecasting Part 3. By Anita Lee-Post. Selecting a forecasting model. Forecast accuracy. A good forecast is accurate but not perfect, i.e., actual value  forecast value Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error

louie
Télécharger la présentation

Forecasting Part 3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ForecastingPart 3 By Anita Lee-Post

  2. Selecting a forecasting model

  3. Forecast accuracy • A good forecast is accurate but not perfect, i.e., actual value  forecast value • Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error • Forecast accuracy has to be monitored by using a “tracking signal”

  4. Overall error measures • Mean absolute deviation (MAD): • Mean squared error (MSE): • The forecast technique giving the lowest MAD/MSE is preferred • MSE magnifies large errors through the squaring process

  5. Tracking signal A way to monitor forecast accuracy is by comparing a measure called: against predetermined control limits (usually +/-4 MAD) in a control chart

  6. Tracking signal continued Signal exceeded limit Tracking signal Upper control limit = +4MAD + 0 0 MAD - Lower control limit = -4MAD Time

  7. Correlation coefficient • Correlation coefficient, r, measures the direction and strength of the linear relationship between the independent (x) and dependent (y) variables

  8. Correlation coefficient continued r = 1 r = -1 Y Y Y = a + b X i i Y = a + b X i i X X r = .89 r = 0 Y Y Y = a + b X Y = a + b X X X i i i i • r = +1: a perfect positive linear relationship • r = 0: no relationship • r = -1: a perfect negative linear relationship

  9. Using Excel for forecasting • Enter the following demand figures for C&A’s product in an Excel worksheet

  10. Using Excel for forecastingcontinued • Invoke the data analysis tool: • Tools  Data Analysis If “Data Analysis” is not found, then • Tools  Add-ins  select “Analysis ToolPak”

  11. Using Excel for forecastingcontinued 3. Select “Moving Average” from the list of data analysis options” to compute a 3-month moving average:

  12. Using Excel for forecastingcontinued 4. Fill in the Moving Average Parameters: • Input Range: cell range of the time series • Labels in First Row: leave it unchecked if your cell range above contains data points only • Interval: parameter n (number of data points used in moving average computation) • Output Range: starting cell address for forecast values (need to offset the input range by one row)

  13. Using Excel for forecastingcontinued • Excel-generated moving average forecasts:

  14. Using Excel for forecastingcontinued 4. Fill in the Exponential Smoothing Parameters: • Input Range: cell range of the time series • Damping factor: 1-a, the smoothing constant • Labels: leave it unchecked if your cell range above contains data points only • Output range: starting cell address for forecast values (no offset is needed)

  15. Using Excel for forecastingcontinued • Excel-generated exponential smoothing forecasts: Copy the formula in cell C7 to cell C8 to compute the forecast for July

  16. Using Excel for forecastingcontinued 4. Fill in the Regression Parameters: • Input Y Range: cell range of the dependent variable • Input X Range: cell range of the independent variable • Labels: have it checked as column headings are included in our input ranges • Output range: starting cell address for regression analysis output

  17. Excel-generated regression analysis report: Enter the formula =D17+D18*A8 in cell B8 to compute the forecast for July

  18. Excel can be used to compute MAD and MSE:

  19. Excel can be used to compute MAD and MSE:

  20. Excel can be used to compute Tracking Signals:

More Related