Motion Picture Revenue Prediction

An Artificial Neural Network Method for Predicting Opening Weekend

Box-Office Revenue

ECE 539 – Fall 2001

Final Project

Chad M. Steighner

ID: 253-699-5562

Concept

Use opening weekend revenues from 1989 through 2000 to train a MLP with back-propagation for classification into 5 classes.

Input features include: Genre, MPAA rating, Date, # Screens, Critical Rating,

Distributor, Run-time and Weekend Length

The MLP is then tested with motion picture data from 2001.

Implementation

Found 473 films with opening weekend data (www.boxofficeguru.com)

Used www.imdb.com to obtain add’l fields

Created Parsedata.java to construct TrainingData and TestingData for MLP

• 432 Training (1989 – 2000)

• 41 Testing (2001)

Through testing and 3-way cross validation found the best set-up to be:

3 Layer MLP (1 hidden) 6 hidden neurons Learning rate = 0.1 Momentum = 0.9 1000 Epochs

Results

45.6% avg. class. of 2001 films. (5.87 St.dev) $10-$12M, $12-$14M, $14-$17M, $17-$28M, $28M+

No Exact Replica Baseline Study: Nat’l Research Group (LA)

- telephone surveys to within 5% of opening weekend revenue. Moviefone claims to be even closer (movie info website) Prof. Arthur De Vany (UC-Irvine)

Bose-Einstein distribution of particles falling into urns. Equally likely particles (audience) will fall into a few urns

(movies) as it is for them to be distributed in any other way.