A total sales forecasting method for a new short life-cycle product in the pre-market period based on an improved evidence theory: application to the film industry

References

• Ahmed, U., H. Waqas, and M. T. Afzal. 2020. “Pre-production Box-office Success Quotient Forecasting.” Soft Computer 8 (24): 6635–6653. doi:https://doi.org/10.1007/s00500-019-04303-w.
   Web of Science ®Google Scholar
• Basallo-Triana, M. J., J. A. Rodríguez-Sarasty, and H. D. Benitez-Restrepo. 2017. “Analogue-based Demand Forecasting of Short Life-cycle Products: A Regression Approach and a Comprehensive Assessment.” International Journal of Production Research 55 (8): 2336–2350. doi:https://doi.org/10.1080/00207543.2016.1241443.
   Web of Science ®Google Scholar
• Benavoli, A., L. Chisci, A. Farina, and B. Ristic. 2008. “Modelling Uncertain Implication Rules in Evidence Theory.” International Conference on information fusion, Cologne, June 30–July 3.
   Google Scholar
• Benkachcha, S., H. El Hassani, É. H. Des Travaux, Km. Publics, and R. EI Jadida. 2014. “Causal Method and Time Series Forecasting Model Based on Artificial Neural Network.” International Journal of Computer Applications 75 (7): 37–42. doi:https://doi.org/10.5120/13126-0482.
   Google Scholar
• Boudaren, M. E. Y., and W. Pieczynski. 2016. “Dempster-Shafer Fusion of Evidential Pairwise Markov Chains.” IEEE Transactions on Fuzzy Systems 24 (6): 13–29. doi:https://doi.org/10.1109/tfuzz.2016.2543750.
   Web of Science ®Google Scholar
• Boujelben, M. A., Y. De Smet, A. Frikha, and H. Chabchoub. 2011. “A Ranking Model in Uncertain, Imprecise and Multi-Experts Contexts: The Application of Evidence Theory.” International Journal of Approximate Reasoning 52 (8): 1171–1194. doi:https://doi.org/10.1016/j.ijar.2011.06.008.
   Web of Science ®Google Scholar
• Chou, C. C., L. J. Liu, S. F. Huang, J. M. Yih, and T. C. Han. 2011. “An Evaluation of Airline Service Quality Using the Fuzzy Weighted SERVEQUAL Method.” Applied Soft Computing 11 (2): 2117–2128. doi:https://doi.org/10.1016/j.asoc.2010.07.010.
   Web of Science ®Google Scholar
• Deng, X., Q. Liu, Y. Deng, and S. Mahadevan. 2016. “An Improved Method to Construct Basic Probability Assignment Based on the Confusion Matrix for Classification Problem.” Information Sciences 340 (1): 250–261. doi:https://doi.org/10.1016/j.ins.2016.01.033.
   Web of Science ®Google Scholar
• Dubois, D., and H. Prade. 1988. “Representation and Combination of Uncertainty with Belief Functions and Possibility Measures.” Computational Intelligence 4 (3): 244–264. doi:https://doi.org/10.1111/j.1467-8640.1988.tb00279.x.
   Google Scholar
• Eliashberg, J., S. K. Hui, and Z. J. Zhang. 2014. “Assessing Box Office Performance Using Movie Scripts: A Kernel-based Approach.” IEEE Transactions on Knowledge and Data Engineering 26 (11): 2639–2648. doi:https://doi.org/10.1109/TKDE.2014.2306681.
   Web of Science ®Google Scholar
• Ghiassi, M., D. Lio, and B. Moon. 2015. “Pre-production Forecasting of Movie Revenues with a Dynamic Artificial Neural Network.” Expert Systems with Applications 42 (6): 3176–3193. doi:https://doi.org/10.1016/j.eswa.2014.11.022.
   Web of Science ®Google Scholar
• Goodwin, P., S. Meeran, and K. Dyussekeneva. 2014. “The Challenges of Pre-launch Forecasting of Adoption Time Series for New Durable Products.” International Journal of Forecasting 30 (4): 1082–1097. doi:https://doi.org/10.1016/j.ijforecast.2014.08.009.
   Web of Science ®Google Scholar
• Graves, A., and J. Schmidhuber. 2005. “Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures.” Neural Networks 18 (5–6): 602–610. doi:https://doi.org/10.1016/j.neunet.2005.06.042.
   PubMed Web of Science ®Google Scholar
• Han, D., Y. Deng, and C. Han. 2011. “Conflicting Evidence Combination by Using Uncertainty Degree.” Control Theory & Applications 28 (6): 788–792. doi:https://doi.org/10.7641/j.issn.1000-8152.2011.6.ccta100806.
   Google Scholar
• Han, D., C. Han, and Y. Deng. 2013. “Novel Approaches for the Transformation of Fuzzy Membership Function into Basic Probability Assignment Based on Uncertainty Optimization.” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21 (02): 289–322. doi:https://doi.org/10.1142/S0218488513500165.
   Web of Science ®Google Scholar
• Hird, A., and L. Kreiling. 2016. “Expert Judgement in Resource Forecasting – The Use of the Delphi Method to Achieve Group Consensus.” Proceedings of the 16th international Conference on Group decision & Negotiation Washington, United States, June 20–24.
   Google Scholar
• Hunter, S. D. III, S. Smith, and S. Singh. 2016. “Predicting Box Office from the Screenplay: A Text Analytical Approach.” Journal of Screenwriting 7 (2): 135–154. doi:https://doi.org/10.1386/josc.7.2.135_1.
   Web of Science ®Google Scholar
• Jousselme, A. L., D. Grenier, and E. Bossé. 2001. “A New Distance Between Two Bodies of Evidence.” Information Fusion 2 (2): 91–101. doi:https://doi.org/10.1016/s1566-2535(01)00026-4.
   Google Scholar
• Kour, V. P., and S. Arora. 2019. “Particle Swarm Optimization Based Support Vector Machine (P-SVM) for the Segmentation and Classification of Plants.” IEEE Access 11 (7): 29374–29385. doi:https://doi.org/10.1109/ACCESS.2019.2901900.
   Web of Science ®Google Scholar
• Litman, B. R. 1982. “Decision-Making in the Film Industry: The Influence of the TV Market.” Journal of Communication 32 (3): 33–52. doi:https://doi.org/10.1111/j.1460-2466.1982.tb02497.x.
   Web of Science ®Google Scholar
• Liu, W. 2006. “Analyzing the Degree of Conflict among Belief Functions.” Artificial Intelligence 170 (11): 909–924. doi:https://doi.org/10.1016/j.artint.2006.05.002.
   Web of Science ®Google Scholar
• Lu, H., F. Chen, M. Xu, M. Chong-jun Wang, and J. Xie. 2016. “Never Ignore the Significance of Different Anomalies: A Cost-Sensitive Algorithm Based on Loss Function for Anomaly Detection.” IEEE International Conference on Tools with Artificial Intelligence, Vietri sul Mare, July 9–11.
   Google Scholar
• Ma, S., R. Fildes, and T. Huang. 2016. “Demand Forecasting with High Dimensional Data: The Case of SKU Retail Sales Forecasting with Intra- and Inter-category Promotional Information.” European Journal of Operational Research 249 (1): 245–257. doi:https://doi.org/10.1016/j.ejor.2015.08.029.
   Web of Science ®Google Scholar
• Marshall, P., M. Dockendorff, and S. Ibáñez. 2013. “A Forecasting System for Movie Attendance.” Journal of Business Research 66 (10): 1800–1806. doi:https://doi.org/10.1016/j.jbusres.2013.01.013.
   Web of Science ®Google Scholar
• Murphy, C. K. 2000. “Combining Belief Functions When Evidence Conflicts.” Decision Support Systems 29 (1): 1–9. doi:https://doi.org/10.1016/s0167-9236(99)00084-6.
   Web of Science ®Google Scholar
• Ouyang, N., Z. Liu, and H. Kang. 2008. “A Method of Distributed Decision Fusion Based on SVM and D-S Evidence Theory.” International Conference on visual information Engineering, Xi’an, 29 July–1 August.
   Google Scholar
• Parikh, C. R., M, J. Pont, and N. B. Jones. 2001. “Application of Dempster–Shafer Theory in Condition Monitoring Applications: A Case Study.” Pattern Recognition Letters 22 (6–7): 777–785. doi:https://doi.org/10.1016/s0167-8655(01)00014-9.
   Web of Science ®Google Scholar
• Rosli, M. F., L. M. Hee, and L. M. Salman. 2015. “Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring.” Applied Mechanics and Materials 773-774 (July): 154–157. www.scientific.net/AMM.773-774.154.
   Google Scholar
• Tang, Y., D. Zhou, S. Xu, and Z. He. 2017. “A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.” Sensors 17 (4): 928–943. doi:https://doi.org/10.3390/s17040928.
   PubMed Web of Science ®Google Scholar
• Van Birgelen, M., K. De Ruyter, and M. Wetzels. 2001. “What Factors Determine Use of Quality-Related Marketing Research Information? An Empirical Investigation.” Total Quality Management 12 (4): 521–534. doi:https://doi.org/10.1080/09544120123611.
   Web of Science ®Google Scholar
• Wang, H., W. Li, X. Qian, and M. Yang. 2016. “An Improved Jousselme Evidence Distance.” Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems 646 (1): 112–120. doi:https://doi.org/10.1007/978-981-10-2672-0_12.
   Google Scholar
• Xin, G., Y. Xiao, and Y. He. 2008. “Study on Algorithms of Determining Basic Probability Assignment Function in Dempster-Shafer Evidence Theory.” 2008 International Conference on machine learning and Cybernetics, Kunming, China, August 12–15. doi:https://doi.org/10.1109/ICMLC.2009.4620390.
   Google Scholar
• Yager, R. R. 1987. “On the Dempster-Shafer Framework and New Combination Rules.” Information Sciences 41 (2): 93–137. doi:https://doi.org/10.1016/0020-0255(87)90007-7.
   Web of Science ®Google Scholar
• Yang, Y., and R. Ou. 2013. “A Study on the Relationship among the Leading Actors, Directors, and the Box Office Income of a Film – Based on Multiple Linear Regression Model.” 2013 6th international Conference on information management, Innovation management and Industrial Engineering (ICIII). IEEE, November 23–24 ..doi:https://doi.org/10.1109/ICIII.2013.6702975.
   Google Scholar
• Zadeh, L. A. 1986. “A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination.” AI Magazine 7 (2): 85–90. doi:https://doi.org/10.1142/9789814261302_0033.
   Google Scholar
• Zhang, X., Y. Deng, F. T. S. Chan, P. Xu, S. Mahadevan, and Y. Hu. 2013. “IFSJSP: a Novel Methodology for the job-Shop Scheduling Problem Based on Intuitionistic Fuzzy Sets.” International Journal of Production Research 51 (17): 5100–5119. doi:https://doi.org/10.1080/00207543.2013.793425.
   Web of Science ®Google Scholar
• Zhang, X., G. Hou, and W. Dong. 2017. “Modelling Movie Attendance with Seasonality: Evidence from China.” Applied Economics Letters 24 (19): 1351–1357. doi:https://doi.org/10.1080/13504851.2017.1279260.
   Web of Science ®Google Scholar
• Zhang, R., F. Meng, Y. Zhou, and B. Liu. 2018. “Relation Classification via Recurrent Neural Network with Attention and Tensor Layers.” Big Data Mining and Analytics 1 (3): 234–244. doi:https://doi.org/0.2018-03-005.
   Google Scholar
• Zheng, J., and S. Zhou. 2014. “Modeling on Box-Office Revenue Prediction of Movie Based on Neural Network.” Journal of Computer Applications 34 (3): 742–748. doi:https://doi.org/10.11772/j.issn.1001-9081.2014.03.0742.
   Google Scholar
• Zhou, J., Y. Zhu, and W. Tang. 2009. “Approach of Expert Opinion Acquisition Based on Cloud Model and Evidence Theory.” 2009 International Conference on Management and Service Science, Wuhan, September 20–22.
   Google Scholar