| WU Zhenfeng（吴振峰）*,WANG Mengmeng**,LAN Tian*,ZHANG Anyuan***.[J].高技术通讯(英文),2023,29(2):122~129 |
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| GHM FKNN:a generalized Heronian mean based fuzzy k nearest neighbor classifier for the stock trend prediction |
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| DOI：10. 3772/ j. issn. 1006-6748. 2023. 02. 002 |
| 中文关键词: |
| 英文关键词: stock trend prediction, Heronian mean, fuzzy k nearest neighbor (FKNN) |
| 基金项目: |
| Author Name | Affiliation | | WU Zhenfeng（吴振峰）* | （*Institute of Scientific and Technical Information of China, Beijing 100038, P.R.China）
（**School of Economics, Renmin University of China, Beijing 100872, P.R.China）
（***Shandong Provincial Center for Quality Control of Feed and Veterinary Drug, Jinan 250022, P.R.China） | | WANG Mengmeng** | （*Institute of Scientific and Technical Information of China, Beijing 100038, P.R.China）
（**School of Economics, Renmin University of China, Beijing 100872, P.R.China）
（***Shandong Provincial Center for Quality Control of Feed and Veterinary Drug, Jinan 250022, P.R.China） | | LAN Tian* | （*Institute of Scientific and Technical Information of China, Beijing 100038, P.R.China）
（**School of Economics, Renmin University of China, Beijing 100872, P.R.China）
（***Shandong Provincial Center for Quality Control of Feed and Veterinary Drug, Jinan 250022, P.R.China） | | ZHANG Anyuan*** | （*Institute of Scientific and Technical Information of China, Beijing 100038, P.R.China）
（**School of Economics, Renmin University of China, Beijing 100872, P.R.China）
（***Shandong Provincial Center for Quality Control of Feed and Veterinary Drug, Jinan 250022, P.R.China） |
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| 中文摘要: |
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| 英文摘要: |
| Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest (RF), probabilistic random forest (PRF), k nearest neighbor (KNN), and fuzzy KNN (FKNN), have difficulty in accurately predicting the stock trend (uptrend or downtrend) for a given date, a generalized Heronian mean (GHM) based FKNN predictor named GHM FKNN was proposed.GHM FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM FKNN achieved the best performance with accuracy of 6237% for AAPL, 5825% for AMZN, and 6410% for NFLX. |
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