Forecasting Gate Receipts Using Neural Networks And Rough Sets

chickenchairwomanAI and Robotics

Oct 19, 2013 (3 years and 7 months ago)

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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