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Improving Forecast Accuracy by Unconstraining Censored Demand Data Rick Zeni AGIFORS Reservations and Yield Management S

Improving Forecast Accuracy by Unconstraining Censored Demand Data Rick Zeni AGIFORS Reservations and Yield Management Study Group May, 2001. Inventory Controls Cause the Censoring. Booking Limit. Cost of Using Censored Data. Forecasts are too low

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Improving Forecast Accuracy by Unconstraining Censored Demand Data Rick Zeni AGIFORS Reservations and Yield Management S

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  1. Improving Forecast Accuracy by Unconstraining Censored Demand DataRick ZeniAGIFORS Reservations and Yield Management Study GroupMay, 2001

  2. Inventory Controls Cause the Censoring Booking Limit

  3. Cost of Using Censored Data • Forecasts are too low • Too few seats are protected for high-fare passengers • Revenue is lost

  4. Methods for Handling Censored Data • Ignore the censoring • Discard the censored dataUnconstrain the Data: • Mean Imputation Method • Booking Profile Method • EM Algorithm • Projection-Detruncation Method

  5. No unconstraining needed May be appropriate if few observations are censored Forecasts may have a positive or negative bias Ignore the Censoring

  6. Simple to implement Fast processing Results in negative bias Discard Censored Observations

  7. Mean Imputation Method • Compare constrained values with the mean from uncensored observations • If the mean is greater than the constrained value, the censored data is replaced (imputed) with the mean

  8. Booking Profile Method • Estimate the shape of the booking profile for flights that have no constrained data points • Choose a starting point where the booking data represents unconstrained demand • Scale the shape of the booking profile to higher levels of demand

  9. Constrained and Unconstrained Booking Profiles

  10. Expectation-Maximization Algorithm Given a distribution assumption and constrained observation C, the best estimate of the unconstrained value is

  11. Expectation-Maximization Algorithm • Step 0: Obtain initial estimates of • Step 1(E-step): Replace all censored observations with their expected values • Step 2: (M-Step): Re-estimate given the new unconstrained data (maximizing the expected likelihood) • Repeat steps 1 and 2 until convergence

  12. Projection Detruncation • Similar to the EM algorithm • Differs mainly in the way the expected values are calculated • There is an additional parameter that affects the aggressiveness of the unconstraining

  13. A B Booking Limit Projection A Projection-Detruncation • The underlying idea is that the probability of underestimating demand is known and constant • Observations that fall to the right of the booking limit represent censored data

  14. A B Booking Limit Projection A Projection-Detruncation • An underestimate of the projected value is indicated by area B • The probability of an underestimate is given by

  15. Which Method Works Best?

  16. Test Data-Common Approach • Choose uncensored data and artificially constrain demand to simulate censored data • The choice of constraining techniques will influence which unconstraining method works best

  17. Test Data-My Approach • Collect actual demand data that has not been censored • Calculate booking limits using a reduced aircraft capacity • Compare the booking limits with the actual demand and determine where the data has been censored • Construct a censored data set that is an accurate representation of true demand behavior

  18. Performance Measurement Each method is evaluated based on the reduction of the error from the baseline method (Ignoring the censoring)

  19. Results for the Ignore Method (baseline)Distribution of Errors of the Observations for the Ignore Method Applied to High Demand Flights

  20. Results for the Discard MethodDistribution of Errors of the Means for the Discard Method Applied to High Demand Flights

  21. Results for the Mean Imputation MethodDistribution of Errors of theObservations for the Mean Imputation Method Applied to High Demand Flights

  22. Results for the Booking Profile MethodDistribution of Errors of the Observations for the Booking Profile Method Applied to High Demand Flights

  23. Results for the EM AlgorithmDistribution of Errors of the Observations for the EM Algorithm Applied to High Demand Flights

  24. EM Algorithm Convergence Rate

  25. Extended EM Algorithm • Produce unconstrained estimates of all censored observations using the EM algorithm • Calculate the mean of demand at the market O&D / fare class / review point level from uncensored observations only • If all the observations in the sample are censored, compare the unconstrained estimate from step 1 with the mean from step 2. If the mean is greater than the estimate, the mean becomes the estimate. Otherwise, do nothing

  26. Results for the Extended EMDistribution of Errors of the Observations for the Extended EM Algorithm Applied to Low Demand Flights

  27. Results for Projection DetruncationDistribution of Errors of the Observations for the PD Algorithm Applied to High Demand Flights

  28. Overall Comparison

  29. Summary • It is better to do nothing than to discard the censored observations • EM algorithm produces the best error reduction • Simulated data showed similar results

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