Machine learning-based approaches to forecasting international trade
| Author: | Bc. Tomáš Kovařík |
|---|---|
| Year: | 2019 - winter |
| Leaders: | Ing. Vilém Semerák M.A., Ph.D. |
| Consultants: | |
| Work type: | Bachelors |
| Language: | English |
| Pages: | 44 |
| Awards and prizes: | |
| Link: | https://is.cuni.cz/webapps/zzp/detail/191197/ |
| Abstract: | 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 |
