Comparison of Gulf Stream Forecast Model Initialization and Verification Analyses

Gulf Stream and Ring Feature Analyses for Forecast Model Validation*

Journal of Atmospheric and Oceanic Technology ◽

10.1175/1520-0426(1997)014<1366:gsarfa>2.0.co;2 ◽

1997 ◽

Vol 14 (6) ◽

pp. 1366-1378 ◽

Cited By ~ 4

Author(s):

Scott M. Glenn ◽

Michael F. Crowley

Keyword(s):

Model Validation ◽

Gulf Stream ◽

Forecast Model

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Molten ice could block Gulf Stream

Nature ◽

10.1038/news030414-2 ◽

2003 ◽

Author(s):

Rex Dalton

Keyword(s):

Gulf Stream

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Slow gulf stream during ice ages?

Nature ◽

10.1038/news991209-10 ◽

1999 ◽

Author(s):

Heike Langenberg

Keyword(s):

Gulf Stream ◽

Ice Ages

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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