Name
Abstract | ||
|---|---|---|
Country risk is an important factor influencing the international investments and transactions. Forecasting country risks of host countries are crucial for investors to make investment strategies and decisions. Considering the complexity and nonlinearity of country risk, this paper proposes a hybrid forecasting model based on empirical mode decomposition (EMD) and extreme learning machine (ELM). Firstly, the original data is decomposed into several different frequency components using EMD. Then, ELM is used to predict the components of different scales respectively, and finally, final country risk forecasting values are integrated. Taking BRICS countries as sample, empirical results show that the EMD-ELM approach performs better than the single prediction models such as ARIMA, SVR and ELM. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.10.219 | Procedia Computer Science |
Keywords | Field | DocType |
Country risk,Empirical mode decomposition,Extreme learning machine,Forecasting | Econometrics,Computer science,Investment strategy,Extreme learning machine,Country risk,Autoregressive integrated moving average,Artificial intelligence,Predictive modelling,Machine learning,Hilbert–Huang transform | Conference |
Volume | ISSN | Citations |
139 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianqian Feng | 1 | 0 | 1.69 |
| Jun Wang | 2 | 20 | 1.40 |
| Xiaolei Sun | 3 | 66 | 13.22 |