1. Design and Implementation of a Dynamic Data MLP to Predict Motion Picture Revenue David A. Gerasimow

2. Problem Statement Problem: Motion picture revenue is seemingly unpredictable. Problem: Motion picture revenue is seemingly unpredictable. Solution: Develop an artificial neural network that takes into account the characteristics of successful films and predicts the opening weekend box-office revenue of upcoming releases. Solution: Develop an artificial neural network that takes into account the characteristics of successful films and predicts the opening weekend box-office revenue of upcoming releases. However, the film industry is constantly changing as is public taste. However, the film industry is constantly changing as is public taste. Consequently, develop dynamic data artificial neural network that is constantly retraining itself to the most up-to-date data. Consequently, develop dynamic data artificial neural network that is constantly retraining itself to the most up-to-date data.

3. Data Collection 1 Determine the significant characteristics of a film that contribute to its success or failure at the box-office. Determine the significant characteristics of a film that contribute to its success or failure at the box-office. The characteristics include: The characteristics include: 1) Month and year of release2) Genre 3) Rating (i.e., G, PG, etc.)4) Runtime 5) Number of theatres in which the film is played 6) Production studio 7) Holiday weekend opening? 8) Sequel? 9) Color, black and white, or animation

4. Data Collection 2 All films released since 1989 that earned more than fifteen million dollars can be found at: www.boxofficeguru.com All films released since 1989 that earned more than fifteen million dollars can be found at: www.boxofficeguru.com Furthermore, film specific information (i.e., genre, etc.) is listed at: Furthermore, film specific information (i.e., genre, etc.) is listed at:www.imdb.com Data collection application development (in Visual Basic 6.0) Data collection application development (in Visual Basic 6.0) dataextractor.exe extracts information from files downloaded from www.boxofficeguru.com and converts them to a readable format. dataextractor.exe extracts information from files downloaded from www.boxofficeguru.com and converts them to a readable format. dataconcatenator.exe links the readable files into a single file. dataconcatenator.exe links the readable files into a single file. dataconverter.exe searches single data file to determine which data fields need to be filled in manually at www.imdb.com dataconverter.exe searches single data file to determine which data fields need to be filled in manually at www.imdb.com This data collection process needs to be performed only once and using it to design an ANN will create a standard static data neural network. This data collection process needs to be performed only once and using it to design an ANN will create a standard static data neural network.

5. Dynamic Data Collection Develop an application that will gather data continually and automatically – allowing ANN to be retrained using up-to-date data. Develop an application that will gather data continually and automatically – allowing ANN to be retrained using up-to-date data. updatewizard.exe (developed in Visual Basic 6.0) updatewizard.exe (developed in Visual Basic 6.0) Functionality of updatewizard.exe Functionality of updatewizard.exe Step 1: Download up-to-date data from www.boxofficeguru.com, process and concatenate. Step 1: Download up-to-date data from www.boxofficeguru.com, process and concatenate. Step 2: Compare up-to-date data to current data. If there is a difference, ANN needs to be retrained. Step 2: Compare up-to-date data to current data. If there is a difference, ANN needs to be retrained. Step 3: Create new training and testing files from up-to-date data. Step 3: Create new training and testing files from up-to-date data.

6. Developing ANN For motion picture revenue application, MLP is appropriate. For motion picture revenue application, MLP is appropriate. Determine optimal MLP configuration using: Determine optimal MLP configuration using: Three-way cross-validation Three-way cross-validation Multiple trials of MLP training Multiple trials of MLP training Compute mean and standard deviation of classification rates to choose configuration. Compute mean and standard deviation of classification rates to choose configuration.

7. MLP Configuration After three-way cross-validation and multiple trials, the results were: After three-way cross-validation and multiple trials, the results were: 10-6-X configuration (X represents the number of output classes – varies depending on options chosen in updatewizard.exe) 10-6-X configuration (X represents the number of output classes – varies depending on options chosen in updatewizard.exe) Learning rate: α = 0.1 Learning rate: α = 0.1 Momentum constant: μ = 0.7 Momentum constant: μ = 0.7 Max. number of epochs: 5000 Max. number of epochs: 5000 Samples per epoch: 64 Samples per epoch: 64 Scaling of input: [-5, 5] Scaling of input: [-5, 5] Other values are defaults as specified in bp.m Other values are defaults as specified in bp.m

8. MATLAB Files for MLP Project MATLAB m-files modified from Professor Yu Hen Hu’s code for back-propagation MLP. Project MATLAB m-files modified from Professor Yu Hen Hu’s code for back-propagation MLP. Modified code contained in: Modified code contained in: moviesbp.m moviesbp.m moviesbptest.m moviesbptest.m moviesbpconfig.m moviesbpconfig.m Modification allows for: Modification allows for: application specific characteristics application specific characteristics hard-coding of configuration hard-coding of configuration interfaces with Windows application to predict opening weekend revenue of a newly-released film interfaces with Windows application to predict opening weekend revenue of a newly-released film

9. Prediction Windows application newmovie.exe allows user to enter a newly-released film’s characteristics using a graphical user interface. Windows application newmovie.exe allows user to enter a newly-released film’s characteristics using a graphical user interface. newmovie.exe stores data in testsinglemovie.txt – which is read by moviesbp.m. Then, the moviesbp.m classifies the film. newmovie.exe stores data in testsinglemovie.txt – which is read by moviesbp.m. Then, the moviesbp.m classifies the film.

10. Results 1 MLP Classification Rates: 54% - 59% MLP Classification Rates: 54% - 59% Improvement over past ANN approaches used by students in CS/ECE/ME 539. Improvement over past ANN approaches used by students in CS/ECE/ME 539. Random classification: Roughly 20% Random classification: Roughly 20% Clearly, MLP performs well. Clearly, MLP performs well. Dynamic Data Aspect Dynamic Data Aspect Data is updated weekly on www.boxofficeguru.com. Run updatewizard.exe to update automatically. Data is updated weekly on www.boxofficeguru.com. Run updatewizard.exe to update automatically.

11. Results 2 The project was functional for less than two weeks. The project was functional for less than two weeks. Thus, not enough time has past to accumulate enough data to make a statistically significant improvements in MLP performance. Thus, not enough time has past to accumulate enough data to make a statistically significant improvements in MLP performance. According to dynamic data ANN model, performance should increase gradually over time. According to dynamic data ANN model, performance should increase gradually over time.