Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10614-023-10460-w.pdf
Reference28 articles.
1. Abdollahi, H. (2020). A novel hybrid model for forecasting crude oil price based on time series decomposition. Applied Energy, 267, 1–11. https://doi.org/10.1016/j.apenergy.2020.115035
2. Abdollahi, H., & Ebrahimi, S. B. (2020). A new hybrid model for forecasting brent crude oil price. Energy, 200, 1–13. https://doi.org/10.1016/j.energy.2020.117520
3. Cen, Z., & Wang, J. (2019). Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy, 169, 160–171. https://doi.org/10.1016/j.energy.2018.12.016
4. Chai, J., Xing, L. M., Zhou, X. Y., Zhang, Z. G., & Li, J. X. (2018). Forecasting the WTI crude oil price by a hybrid-refined method. Energy Economics, 71, 114–127. https://doi.org/10.1016/j.eneco.2018.02.004
5. Chai, J., Wang, Y., Wang, S., & Wang, Y. (2019). A decomposition integration model with dynamic fuzzy reconstruction for crude oil price prediction and the implications for sustainable development. Journal of Cleaner Production, 229, 775–786.
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