A Two-Stage Machine Learning Approach to Forecast the Lifetime of Movies in a Multiplex

  • Abhijith RagavEmail author
  • Sai Vishwanath Venkatesh
  • Ramanathan Murugappan
  • Vineeth Vijayaraghavan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)


Collecting over $2.1 billion annually, the cinema exhibition industry contributes 55% of the total revenue towards the Indian film industry. Selection of films is one of the most economically crucial decisions in cinema exhibition. Film selection is incredibly complicated to execute in India owing to its diverse demographic across regions and the resulting behavioral complexity. Working with data from one of India’s leading multiplexes, the authors offer a two-stage solution using machine learning to predict if a movie would proceed to be screened in the following week and the number of weeks it would continue to be screened if it does. The estimation of a movie’s lifetime helps exhibitors to make intelligent negotiations with distributors regarding screening and scheduling. The authors introduce a new metric MLE to evaluate the error in predicting the remaining lifetime of a film. The approach proposed in this paper surpasses the existing system of lifetime prediction and consequent selection of movies, which is currently performed based on intuition and heuristics.


Machine learning Feature engineering Movie lifetime forecasting Film industry 


  1. 1.
    Deloitte: Economic Contribution of the Film ad Television Industry in India, 2017 (2018). Accessed from
  2. 2.
    Eliashberg, J., Hegie, Q., Ho, J., Huisman, D., Miller, S.J., Swami, S., Weinberg, C.B., Wierenga, B.: Demand-driven scheduling of movies in a multiplex. Int. J. Res. Mark. 26(2), 75–88 (2009). ISSN 0167-8116CrossRefGoogle Scholar
  3. 3.
    Sivasantoshreddy, A., Kasat, P., Jain, A.: Box-Office opening prediction of movies based on hype analysis through data mining. Int. J. Comput. Appl. 56(1), 1–5 (2012)Google Scholar
  4. 4.
    Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. Exp. Syst. Appl. 30(2), 243–254 (2006)CrossRefGoogle Scholar
  5. 5.
    Jaiswal, S.R., Sharma, D.: Predicting success of bollywood movies using machine learning techniques. In: Proceedings of the 10th Annual ACM India Compute Conference (2017)Google Scholar
  6. 6.
    Ainslie, A., Drèze, X., Zufryden, F.: Modeling movie life cycles and market share. Mark. Sci. 24(3), 508–517 (2005)CrossRefGoogle Scholar
  7. 7.
    Ganesan, V.A., Divi, S., Moudhgalya, N.B., Sriharsha, U., Vijayaraghavan, V.: Forecasting food sales in a multiplex using dynamic artificial neural networks. In: Arai, K., Kapoor, S. (eds.) Advances in Computer Vision. CVC: Advances in Intelligent Systems and Computing, vol. 944. Springer, Cham (2019). (2020)Google Scholar
  8. 8.
    Huang, L., Liu, X., Liu, Y., Lang, B., Tao, D.: Centered Weight Normalization in Accelerating Training of Deep Neural Networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2822–2830 (2017)Google Scholar
  9. 9.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929-1958 (2014).
  10. 10.
    Geurts, P., Ernst, D. Wehenkel, L. Mach Learn, vol. 63, no. 3 (2006).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abhijith Ragav
    • 1
    Email author
  • Sai Vishwanath Venkatesh
    • 1
  • Ramanathan Murugappan
    • 2
  • Vineeth Vijayaraghavan
    • 3
  1. 1.SRM Institute of Science and TechnologyChennaiIndia
  2. 2.Madras Institute Of TechnologyChennaiIndia
  3. 3.Solarillion FoundationChennaiIndia

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