Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting-Reference-Cited by-同舟云学术

Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting

Published:2022-07-19 Issue:7 Volume:53 Page:7599-7613
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Jiang Liyuan,Tao Zhifu^ORCID,Zhu Jiaming,Zhang Junting,Chen Huayou

Funder

Humanities and Social Sciences Research Youth Project of the Ministry of Education of China

Humanities and Social Science Research Project of Universities in Anhui Province, China

Natural Science Foundation of Anhui Province

National Natural Science Foundation of China

Provincial Natural Science Research Project of Anhui Colleges, China

The teacher project of Anhui Ecology and Economic Development Research Center in 2021

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03835-3.pdf

Reference42 articles.

1. Wang ZC, Chen LR, Ding ZN et al (2020) An enhanced interval PM(2.5) concentration forecasting model based on BEMD and MLPI with influencing factors. Atmospheric Environment 223:117200.1-117200.16

2. Wang ZC, Chen LR, Zhu JM et al (2020) Double decomposition and optimal combination ensemble learning approach for interval-valued AQI forecasting using streaming data. Environ Sci Pollut Res 27 (13):37802–37817

3. Doreswamy H, Kumar Kumar KS, Mohamed Gad I (2020) Time series analysis for prediction of PM2.5 using seasonal autoregressive integrated moving average (SARIMA) model on Taiwan air quality monitoring network data. J Comput Theor Nanosci 17(9):3964–3969

4. Dong M, Yang D, Kuang Y et al (2009) PM2.5 Concentration prediction using hidden semi-Markov model-based times series data mining. Expert Syst Appl 36(5):9046–9055

5. Lai X, Li H, Pan Y (2021) A combined model based on feature selection and support vector machine for PM2.5 prediction. J Intell Fuzzy Syst 40(5):1–15

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