Polynomial Fuzzy Information Granule-Based Time Series Prediction
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
- Mingdi Hu & Chenrui Wang & Jingbing Yang & Yi Wu & Jiulun Fan & Bingyi Jing, 2022. "Rain Rendering and Construction of Rain Vehicle Color -24 Dataset," Mathematics, MDPI, vol. 10(17), pages 1-18, September.
- Jiang, Ping & Yang, Hufang & Li, Hongmin & Wang, Ying, 2021. "A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity," Energy, Elsevier, vol. 219(C).
- Linjie He & Yumin Chen & Caiming Zhong & Keshou Wu, 2022. "Granular Elastic Network Regression with Stochastic Gradient Descent," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
- Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
- Wang, Chen & Zhang, Shenghui & Liao, Peng & Fu, Tonglin, 2022. "Wind speed forecasting based on hybrid model with model selection and wind energy conversion," Renewable Energy, Elsevier, vol. 196(C), pages 763-781.
- Yanfeng Wang & Haohao Wang & Sanyi Li & Lidong Wang, 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
- Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
- Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
- Zheng, Shuxian & Tan, Zhanglu & Xing, Wanli & Zhou, Xuanru & Zhao, Pei & Yin, Xiuqi & Hu, Han, 2022. "A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices," Resources Policy, Elsevier, vol. 78(C).
- He, Zhichao & Huang, Jianhua, 2023. "A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction," Resources Policy, Elsevier, vol. 86(PB).
- Zheng, Xiaolei & Nguyen, Hoang & Bui, Xuan-Nam, 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model," Resources Policy, Elsevier, vol. 74(C).
- Nabavi, Zohre & Mirzehi, Mohammad & Dehghani, Hesam, 2024. "Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis," Resources Policy, Elsevier, vol. 90(C).
- Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
- Choi, Yosoon & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung, 2022. "Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods," Resources Policy, Elsevier, vol. 75(C).
- Mingdi Hu & Yi Wu & Jiulun Fan & Bingyi Jing, 2022. "Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions," Mathematics, MDPI, vol. 10(19), pages 1-16, September.
- Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
- Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
- Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
- Pang, Qinghua & Dong, Xianwei & Zhang, Lina & Chiu, Yung-ho, 2023. "Drivers and key pathways of the household energy consumption in the Yangtze river economic belt," Energy, Elsevier, vol. 262(PA).
- Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).
- Hu, Haisheng & Zhao, Laijun & Dong, Wanhao, 2023. "How to achieve the goal of carbon peaking by the energy policy? A simulation using the DCGE model for the case of Shanghai, China," Energy, Elsevier, vol. 278(PA).
- Ling Yang & Kai Zhao & Yankai Zhao & Mengyuan Zhong, 2021. "Identifying Key Factors in Determining Disparities in Energy Consumption in China: A Household Level Analysis," Energies, MDPI, vol. 14(21), pages 1-20, November.
More about this item
Keywords
polynomial fuzzy information granule; fuzzy inference system; time series prediction;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4495-:d:987055. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.