Abstract
Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that even though the performance of traditional approaches, such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
El-Nasr, M.S., Drachen, A., Canossa, A.: Game Analytics. Sprint, New York (2013)
Yannakakis, G.N., Togelius, J.: Artificial Intelligence and Games. Springer (2017). http://gameaibook.org
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2016)
Adhikari, R., Agrawal, R.: An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013)
Asmussen, S., Glynn, P.W.: Stochastic Simulation: Algorithms and Analysis, vol. 57. Springer Science and Business Media, Heidelberg (2007)
Carson, Y., Maria, A.: Simulation optimization: methods and applications. In: Proceedings of the 29th Conference on Winter Simulation, pp. 118–126. IEEE Computer Society (1997)
Box, G.E., Jenkins, G.M.: Time series analysis: forecasting and control, revised ed. Holden-Day (1976)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models, vol. 43. CRC Press, Boca Raton (1990)
Busseti, E., Osband, I., Wong, S.: Deep learning for time series modeling. Technical report, Stanford University (2012)
Bauckhage, C., Kersting, K., Sifa, R., Thurau, C., Drachen, A., Canossa, A.: How players lose interest in playing a game: an empirical study based on distributions of total playing times. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 139–146. IEEE (2012)
Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K., Bauckhage, C.: Predicting player churn in the wild. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)
Periáñez, Á., Saas, A., Guitart, A., Magne, C.: Churn prediction in mobile social games: towards a complete assessment using survival ensembles. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 564–573. IEEE (2016)
Bertens, P., Guitart, A., Periáñez, Á.: Games and big data: a scalable multi-dimensional churn prediction model. In: Accepted in IEEE CIG (2017)
Bauckhage, C., Drachen, A., Sifa, R.: Clustering game behavior data. IEEE Trans. Comput. Intell. AI Games 7(3), 266–278 (2015)
Drachen, A., Sifa, R., Bauckhage, C., Thurau, C.: Guns, swords and data: clustering of player behavior in computer games in the wild. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp. 163–170. IEEE (2012)
Drachen, A., Thurau, C., Sifa, R., Bauckhage, C.: A comparison of methods for player clustering via behavioral telemetry. arXiv preprint arXiv:1407.3950 (2014)
Sifa, R., Bauckhage, C., Drachen, A.: The playtime principle: large-scale cross-games interest modeling. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)
Saas, A., Guitart, A., Periáñez, Á.: Discovering playing patterns: time series clustering of free-to-play game data. In: 2016 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2016)
Lawrence, K.D., Geurts, M.D.: Advances in Business and Management Forecasting, vol. 4. Emerald Group Publishing, Bingley (2006)
Box, G.E., Cox, D.R.: An analysis of transformations. J. Roy. Statist. Soc. Ser. B (Methodol.) 26, 211–252 (1964)
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)
Cragg, J.G.: Estimation and testing in time-series regression models with heteroscedastic disturbances. J. Econom. 20(1), 135–157 (1982)
Dietterich, T.G.: Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems, pp. 1–15. Springer (2000)
Mason, L., Baxter, J., Bartlett, P.L., Frean, M.R.: Boosting algorithms as gradient descent. In: NIPS, pp. 512–518 (1999)
Breiman, L.: “Arcing the edge,” Technical Report 486, Statistics Department. University of California at Berkeley, Technical report (1997)
Ridgeway, G.: Generalized boosted models: a guide to the gbm package. Update 1(1), 2007 (2007)
Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013)
Zhang, T., Yu, B.: Boosting with early stopping: convergence and consistency. Ann. Stat. 33(4), 1538–1579 (2005)
Hastie, T., Tibshirani, R.: Generalized additive models: some applications. J. Am. Stat. Assoc. 82(398), 371–386 (1987)
Maindonald, J.: Smoothing terms in GAM models (2010)
Larsen, K.: GAM: the predictive modeling silver bullet. Multithreaded. Stitch Fix, vol. 30 (2015)
Chen, C.: Generalized additive mixed models. In: Communications in Statistics-Theory and Methods, vol. 29, no. 5–6, pp. 1257–1271 (2000)
Breslow, N.E., Clayton, D.G.: Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88(421), 9–25 (1993)
Wood, S.N.: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 73(1), 3–36 (2011)
Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends® Sig. Process. 7(3–4), 197–387 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cognit. Sci. 9(1), 147–169 (1985)
Larochelle, H., Bengio, Y.: Classification using discriminative restricted Boltzmann machines. In: Proceedings of the 25th International Conference on Machine Learning, pp. 536–543. ACM (2008)
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42, 11–24 (2014)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)
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)
Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 78. ACM (2004)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2005)
Fox, A.J.: Outliers in time series. J. Roy. Stat. Soc. Ser. B (Methodol.) 11, 350–363 (1972)
Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)
Japan national holidays. https://www.timeanddate.com/holidays/japan/
Tokyo daily temperature 2014 to 2017. https://www.wunderground.com/
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Eilers, P.H., Marx, B.D.: Flexible smoothing with B-splines and penalties. Stat. Sci. 11, 89–102 (1996)
Wood, S.N.: Generalized Additive Models: An Introduction with R. CRC Press, Boca Raton (2017)
Wood, S.N.: Thin plate regression splines. J. R. Stat. Soc. Ser. B Stat. Methodol. 65(1), 95–114 (2003)
Prechelt, L.: Early stopping-but when? In: Neural Networks: Tricks of the trade, pp. 55–69. Springer (1998)
Gilliland, M., Sglavo, U., Tashman, L.: Business Forecasting: Practical Problems and Solutions. Wiley, Hoboken (2016)
Makridakis, S., Hibon, M.: The M3-Competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)
Julkunen, J.: Feature Spotlight: In-Game Events and Market Trends (2016). http://www.gamerefinery.com/in-game-events-market-trends/
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Dwyer, L., Gill, A., Seetaram, N.: Handbook of Research Methods in Tourism: Quantitative and Qualitative Approaches. Edward Elgar Publishing, Cheltenham (2012)
Khandakar, Y., Hyndman, R.J.: Automatic time series forecasting: the forecast Package for R (2008)
Wood, S.N.: MGCV: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation (2012)
Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions, arXiv e-prints, abs/1605.02688, http://arxiv.org/abs/1605.02688, May 2016
Acknowledgments
We thank Sovannrith Lay for helping to gather the data and Javier Grande for his careful review of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Guitart, A., Chen, P.P., Bertens, P., Periáñez, Á. (2019). Forecasting Player Behavioral Data and Simulating In-Game Events. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-03402-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03401-6
Online ISBN: 978-3-030-03402-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)