Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Nosratabadi, Saeed; Mosavi, Amir; Duan, Puhong; Ghamisi, Pedram; Ferdinand, Filip; Shamshirband, Shahaboddin; Reuter, Uwe; Batista, João da Gama; Gandomi, Amir H.

doi:10.2139/ssrn.3711309

SSRN Journal

2020

DOI: 10.2139/ssrn.3711309

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Saeed Nosratabadi

Amir Mosavi²,

Puhong Duan

et al.

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2024

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A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

Şimşek,

Tuğrul,

Çam

et al. 2024

Transport and Telecommunication Journal

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Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

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A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

Şimşek,

Tuğrul,

Çam

et al. 2024

Transport and Telecommunication Journal

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Cited by 1 publication

References 47 publications

A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

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