Machine learning-based approaches to forecasting international trade
Autor: | Bc. Tomáš Kovařík |
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Rok: | 2019 - zimní |
Vedoucí: | Ing. Vilém Semerák M.A., Ph.D. |
Konzultant: | |
Typ práce: | Bakalářská |
Jazyk: | Anglicky |
Stránky: | 44 |
Ocenění: | |
Odkaz: | https://is.cuni.cz/webapps/zzp/detail/191197/ |
Abstrakt: | In this thesis I focus on comparison of gravity model estimated with ordinary least squares and Poisson pseudo-maximum likelihood with regression techniques based on machine learning, namely support vector machines, random forests, and arti_cial neural networks. I discuss the advantages and disadvantages of these approaches and compare their forecasting accuracy on exports data. I demonstrate that random forest models and arti_cial neural networks provide superior forecasting accuracy |