A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction
Li Li and
Additional contact information
Quande Qin: Shenzhen University
Huangda He: Shenzhen University
Li Li: Shenzhen University
Computational Economics, 2020, vol. 55, issue 4, No 11, 1249-1273
Abstract This study proposes a decomposition-ensemble based carbon price forecasting model, which integrates ensemble empirical mode decomposition (EEMD) with local polynomial prediction (LPP). The EEMD method is used to decompose carbon price time series into several components, including some intrinsic mode functions (IMFs) and one residue. Motivated by the fully local characteristics of a time series decomposed by EEMD, we adopt the traditional LPP and regularized LPP (RLPP) to forecast each component. This led to two forecasting models, called the EEMD-LPP and EEMD-RLPP, respectively. Based on the fine-to-coarse reconstruction principle, an auto regressive integrated moving average (ARIMA) approach is used to forecast the high frequency IMFs, and LPP and RLPP is applied to forecast the low frequency IMFs and the residue. The study also proposes two other forecasting models, called the EEMD-ARIMA-LPP and EEMD-ARIMA-RLPP. The empirical study results showed that the EEMD-LPP and EEMD-ARIMA-LPP outperform the two other models. Furthermore, we examine the robustness and effects of parameter settings in the proposed model. Compared with existing state-of-art approaches, the results demonstrate that EEMD-ARIMA-LPP and EEMD-LPP can achieve higher level and directional predictions and higher robustness. The EEMD-LPP and EEMD-ARIMA-LPP are promising approaches for carbon price forecasting.
Keywords: Carbon price forecasting; Ensemble empirical mode decomposition; Decomposition-ensemble framework; Local polynomial prediction (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s10614-018-9862-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:55:y:2020:i:4:d:10.1007_s10614-018-9862-1
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().