Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine-Reference-Cited by-同舟云学术

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Published:2021-10-14 Issue: Volume: Page:
ISSN:0277-6693
Container-title:Journal of Forecasting
language:en
Short-container-title:Journal of Forecasting

Author:

Zhang Wen¹,Wu Zhibin¹^ORCID

Affiliation:

1. Business School Sichuan University Chengdu China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2831

Reference56 articles.

1. Carbon pricing in climate policy: seven reasons, complementary instruments, and political economy considerations;Baranzini A.;Wiley Interdisciplinary Reviews: Climate Change,2017

2. Generalized autoregressive conditional heteroskedasticity;Bollerslev T.;Journal of Econometrics,1986

3. Particle swarm optimization for single objective continuous space problems: A review;Bonyadi M. R.;Evolutionary Computation,2017

4. Carbon pricing, technology transition, and skill‐based development;Borissov K.;European Economic Review,2019

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