Download
1 / 13
130 likes | 250 Views
Validation and Monitoring. Measures of Accuracy Combining Forecasts Managing the Forecasting Process Monitoring & Control. Technique Adequacy. Are the autocorrelation coefficients of the residuals random? Calculate MAD, MSE, MAPE, & MPE Are the residuals normally distributed?
E N D
Validation and Monitoring • Measures of Accuracy • Combining Forecasts • Managing the Forecasting Process • Monitoring & Control
Technique Adequacy • Are the autocorrelation coefficients of the residuals random? • Calculate MAD, MSE, MAPE, & MPE • Are the residuals normally distributed? • Is the technique simple to use?
Accuracy • Forecasts of groups (for example, by region) are more accurate than forecasts of individual items. Item Actual Forecast Error Percent Error 1 1000 900 100 10% 2 900 1000 -100 -11 3 900 1000 -100 -11 4 1200 1000 200 16.7 4000 3900 100 2.5
Tracking • Beware of GIGO • Large outliers need to be managed - and usually removed • Tracking signals monitor accuracy and identify when intervention is required • When a forecast goes out of control, bias occurs as the errors are primarily positive or negative
Combining Forecasts • There is not one best method of forecasting • Research has shown that forecasts that are averages of other good forecasts are on average better than individual forecasts. • Simple and weighted averages
Combining Methods • If individual methods are unbiased, combined forecast should also be unbiased. • Do not use simple averages when there is a large difference between the variances of the errors. • With weighted averages, higher weights should be assigned to those forecasts that have the lowest error variance. Sum of the weights needs to equal 1. • Can determine weights as the inverse of the squared errors (accuracy and errors are inversely related) • Can determine weights through regression analysis. Models are the independent variables - problematic in that the constant term may not equal 0 and the regression coefficients may not sum to 1.
Past Research on Combining • Process of combining improves accuracy even when compared to single best model • Simple non-weighted averages works well • When one model is considerably better - do not combine, drop the inferior model • In some cases, there are no gains from combining
Recent Research on Combining • Arinze et al (1997) - use a knowledge based system (computer program that includes the knowledge of a human expert) to select a model or combination of models. • Yaylor & Bunn (1999) Hybrid of empirical and theoretical methods that applies quartile regression to empirical fit errors to produce forecast error. • Fisher & Harvey (1999) Results show that providing information about mean absolute percentage errors updated each period enables judges to combine their forecasts in a way that outperforms the simple average. • See Excel output for examples of combining forecasts with averages
Managing the Forecasting Process • Problem definition • Information search • Model formulation • Experimental design • Forecast • Results analysis • Implementation
Managing the Forecasting Process • Need to use common sense - some examples: • In Time Series - need to determine how many time periods to include • Regression - what additional variables can be included to increase R-squared, but maintain parsimony? • Complex forecasting techniques, such as Box-Jenkins that reduce forecast error need to be user friendly - or they won’t be used • Chi-square tests should be used to determine goodness of fit. • Advancement of pc and software has increased the frequency of methods - however, at what cost? Excel’s output of Regression is very professional and easy to incorporate charts - but does not provide autocorrelation analysis
Managing the Forecasting Process • Why is a forecast needed? • Who will use the forecast? • What level of detail or aggregation is required? • What data are available? • Costs with technique and collecting data • How accurate is the forecast expected to be? • How will the forecast be used in the organization? • How will the forecast be evaluated?
Other factors to consider • Selection depends on many factors - content and context of the forecast, availability of historical data, degree of accuracy desired, time periods to be forecast, cost/benefit to the company, time & resources available. • How far in the future are you forecasting? Ease of understanding. How does it compare to other models? • Forecasts are usually incorrect (adjust) • Forecasts should be stated in intervals (estimate of accuracy) • Forecasts are more accurate for aggregate items • Forecasts are less accurate further into the future
