A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction
The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Theref...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/4/440 |
| _version_ | 1827695557704941568 |
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| author | Dingming Wu Xiaolong Wang Shaocong Wu |
| author_facet | Dingming Wu Xiaolong Wang Shaocong Wu |
| author_sort | Dingming Wu |
| collection | DOAJ |
| description | The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)). |
| first_indexed | 2024-03-10T12:28:56Z |
| format | Article |
| id | doaj.art-5cf5cfee6d3d4c989f6cc90fcb396cf5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T12:28:56Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5cf5cfee6d3d4c989f6cc90fcb396cf52023-11-21T14:49:51ZengMDPI AGEntropy1099-43002021-04-0123444010.3390/e23040440A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock PredictionDingming Wu0Xiaolong Wang1Shaocong Wu2College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaCollege of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaCollege of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaThe trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).https://www.mdpi.com/1099-4300/23/4/440stock predictionextreme learning machinewavelet transformdeep learning |
| spellingShingle | Dingming Wu Xiaolong Wang Shaocong Wu A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction Entropy stock prediction extreme learning machine wavelet transform deep learning |
| title | A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction |
| title_full | A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction |
| title_fullStr | A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction |
| title_full_unstemmed | A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction |
| title_short | A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction |
| title_sort | hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction |
| topic | stock prediction extreme learning machine wavelet transform deep learning |
| url | https://www.mdpi.com/1099-4300/23/4/440 |
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