A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

Agarwal, Manav; Venugopal, Shreya; Kashyap, Rishab; Bharathi, R.

doi:10.5121/csit.2021.111802

Cited by 3 publications

(2 citation statements)

References 16 publications

(16 reference statements)

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“…However, the complex, often non-linear relationships between myriad factors and movie box office performance necessitated the adoption of more sophisticated machine learning models. Agarwal et al evaluated the efficacy of diverse models, including machine learning algorithms, time series analysis, and neural networks [5]. Apala et al utilized k-means clustering to categorize movies based on data from Twitter, YouTube, and IMDb, followed by the development of a decision tree classifier prediction model [6].…”

Section: Related Workmentioning

confidence: 99%

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Xie

2024

ACE

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Predicting the revenue of a movie prior to its release presents a significant challenge. The ability to predict pre-release revenue enables movie production companies to devise effective marketing strategies and mitigate the risks associated with potential box office failures. The primary hurdles in this endeavor stem from managing the myriad factors influencing box office outcomes and accurately forecasting a movie's revenue before it becomes available to the public. To overcome these challenges, we introduce a sophisticated Early Movie Box Office Prediction Model that incorporates Ensemble Learning and Feature Encoding techniques. This model amalgamates multiple foundational models, utilizing regression and decision trees to forecast box office revenues. Our composite model demonstrates superior performance over its constituent models, achieving an impressive accuracy rate of 91.4%.

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Section: Related Workmentioning

confidence: 99%

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Xie

2024

ACE

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“…Sharma et al proposed a new method to study the influence of movie stars and directors and used this method to predict the total box office [6]. Agarwal et al positioned the success or failure of accuracy metrics through classification and clustering, analyzing the impact of factors such as movie duration and budget on success [7]. Shahid et al transformed the prediction of total box office into the prediction of return on investment, emphasizing the analysis of the influence of movie genres on film investment [8].…”

Section: Introductionmentioning

confidence: 99%

Research on the prediction method of first-day box office considering the holiday factor

Kuang

2024

ACE

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The prediction of first-day box office has been a hot research topic. However, existing research on movie prediction models lacks analysis and exploration of the holiday factor. In order to uncover the holiday factor contributing to the fluctuation of movie box office, we propose a prediction method for the first-day box office considering the Holiday Factor. Initially, we present a SARIMABox-Cox model for time series analysis. The experimental results reveal significant differences in movie box office during weekends and weekdays, as well as during summer vacation and non-summer vacation periods. Consequently, we introduce two variables, weekend dummy variable and summer vacation dummy variable, as holiday factors. Subsequently, to further investigate the impact of the holiday factor on the accuracy of first-day box office prediction, we integrate the holiday factor with multiple linear regression (MLR), random forest (RF), support vector machine regression (SVR), deep neural network (DNN), and extreme gradient boosting (XGBoost) to establish several first-day box office prediction models. Experimental results on the publicly available dataset Daily Earnings Data for 3400 Movies indicate a substantial improvement in the performance of models considering the holiday factor, with the highest enhancement reaching 16.42%.

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Performance Predictions of Sci-Fi Films via Machine Learning

Al–Fahoum

Ghobon²

2023

Applied Sciences

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The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data.

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A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

Cited by 3 publications

References 16 publications

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Research on the prediction method of first-day box office considering the holiday factor

Performance Predictions of Sci-Fi Films via Machine Learning

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