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用于COVID-19每日确诊病例模糊时间序列预测的分区改进海鸥优化算法与XGBoost预测

Improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed.

作者信息

Xian Sidong, Chen Kaiyuan, Cheng Yue

机构信息

Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R.China.

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China.

出版信息

Adv Eng Softw. 2022 Nov;173:103212. doi: 10.1016/j.advengsoft.2022.103212. Epub 2022 Aug 1.

DOI:10.1016/j.advengsoft.2022.103212
PMID:35936352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9340105/
Abstract

The establishment of fuzzy relations and the fuzzification of time series are the top priorities of the model for predicting fuzzy time series. A lot of literature studied these two aspects to ameliorate the capability of the forecasting model. In this paper, we proposed a new method(FTSOAX) to forecast fuzzy time series derived from the improved seagull optimization algorithm(ISOA) and XGBoost. For increasing the accurateness of the forecasting model in fuzzy time series, ISOA is applied to partition the domain of discourse to get more suitable intervals. We improved the seagull optimization algorithm(SOA) with the help of the Powell algorithm and a random curve action to make SOA have better convergence ability. Using XGBoost to forecast the change of fuzzy membership in order to overcome the disadvantage that fuzzy relation leads to low accuracy. We obtained daily confirmed COVID-19 cases in 7 countries as a dataset to demonstrate the performance of FTSOAX. The results show that FTSOAX is superior to other fuzzy forecasting models in the application of prediction of COVID-19 daily confirmed cases.

摘要

模糊关系的建立和时间序列的模糊化是模糊时间序列预测模型的首要任务。许多文献研究了这两个方面以提高预测模型的能力。在本文中，我们提出了一种新的方法（FTSOAX）来预测基于改进海鸥优化算法（ISOA）和XGBoost的模糊时间序列。为了提高模糊时间序列预测模型的准确性，应用ISOA划分论域以获得更合适的区间。我们借助鲍威尔算法和随机曲线操作改进了海鸥优化算法（SOA），使SOA具有更好的收敛能力。使用XGBoost预测模糊隶属度的变化，以克服模糊关系导致准确性低的缺点。我们获取了7个国家的每日新冠肺炎确诊病例作为数据集来验证FTSOAX的性能。结果表明，在新冠肺炎每日确诊病例预测应用中，FTSOAX优于其他模糊预测模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/b2e4aa037735/gr12_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/b2e4aa037735/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/f94bd94db50c/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/a5426e98675a/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/5b1d9049c751/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/ca558d33e93e/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/83ba2ba7fddb/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/851b58f8b98d/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/35c1b98b81d7/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/d45b819ec72b/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/6f6906d410c8/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/2baf63ff16d8/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/5443f1527211/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/599ac1201b47/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/120a/9340105/b2e4aa037735/gr12_lrg.jpg

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本文引用的文献

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Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19.用于COVID-19模糊时间序列预测的分区粒子群优化与模糊排序
Appl Soft Comput. 2021 Oct;110:107611. doi: 10.1016/j.asoc.2021.107611. Epub 2021 Jun 17.
2
A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India.一种用于预测印度 COVID-19 感染病例和死亡人数的新型混合模糊时间序列模型。
ISA Trans. 2022 May;124:69-81. doi: 10.1016/j.isatra.2021.07.003. Epub 2021 Jul 6.
应用导向疾病诊断模型的元启发式算法设计。
Technol Health Care. 2024;32(6):4041-4061. doi: 10.3233/THC-231755.