Do Eemd based decomposition-ensemble models indeed improve prediction for crude oil futures prices
Bibliographic Data
| ID | 21401336 |
|---|---|
| Authors | Kunliang Xu (0000-0001-5852-9239, University of Science and Technology Beijing), Hongli Niu (0000-0001-5610-1650, University of Science and Technology Beijing, corresponding author) |
| Year | 2022 |
| Volume | 184 |
| Pages | 121967 |
| Publication date | 2022-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technological Forecasting and Social Change (JOURNAL) |
| Journal identifiers | ISSN: 0040-1625 • E-ISSN: 1873-5509 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techfore.2022.121967 |
| OpenAlex | W4293795615 |
| Language | EN |
| References cited | 45 |
Artificial neural network · Decomposition · Econometrics · Economics · Ensemble forecasting · Futures contract · Hilbert–Huang transform · Machine learning · Series (stratigraphy) · West Texas Intermediate · Artificial Intelligence · Computer Science · Energy Load and Power Forecasting · Market Dynamics and Volatility · Mathematics · Stock Market Forecasting Methods
Another look at measures of forecast accuracy
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Ensemble Empirical Mode Decomposition
A novel multiscale forecasting model for crude oil price time series
A novel hybrid approach to forecast crude oil futures using intraday data
An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using Iceemdan-ICA
Comparing Predictive Accuracy
| Highly cited | No |
|---|