Forecasting Gate Receipts Using
Neural Networks And Rough Sets
Ramesh Sharda
Oklahoma State University
Edith Meany
Henry Amato
University of Nevada
-
Reno
Niketu Mithani
Oklahoma State University
Contact e
-
mail: sharda@okstate.edu
Forecasting Gate Receipts:
A Tough Problem
Previous Work
-
Litman (1997)
-
Need Marketing Estimates
-
Sawhney & Eliashberg (1996)
-
Need initial audience data
A Different Approach
•
Treat the problem as a classification
problem
•
Neural Networks and Rough Sets are
appropriate for classification
Neural Network Description
•
Inputs:
Estimated release date (season)
Intensity of competition rating
Rating
Star power
Genre
Technical Effects
Sequel ?
Estimated Screens at opening
•
Output:
Box office gross receipts:
flop blockbuster
Description
Neural Networks
Test 1
•
43 Input neurons
•
9 Output neurons
•
2 hidden layers (18 and 16 neurons)
•
tanh transfer function
•
Backpropagation
Rough Sets
•
Automatic configuration by H. Amato
Data
•
120 films for 1997
•
Twenty pairs of 20 films for training and
testing
Results
Results
Further
Tests
Data
•
1998
-
255 films used
•
Predictors added
-
1 additional genre of
romance/musical
•
Predictors removed
-
perceived competition, star
quality and technical effects
•
Film budget
Test 2
Twenty pairs of training(155) and test(100)
films.
Results
Further
Tests
Test 3
Twenty pairs of training(155) and test(100) films
with no hidden layers used.
Test 4
Twenty pairs of training(80) and test(64) films used
with the budget information included.
Results
Results
Issues
•
Performance Measurement
•
Misclassification Costs
•
Comparison with other models/techniques
Conclusion
•
Neural networks offer a reasonable
predictive capability
•
Rough sets offer a comparative performance
•
Performance measurement issues
•
Inclusion of misclassification costs
•
Comparison with other models/techniques
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