Machine learning-based approaches to forecasting international trade
| Autor: | Bc. Tomáš Kovařík |
|---|---|
| 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 |
