Hybrid preprocessing for neural network-based stock price prediction
In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Trans...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024168508 |
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| author | Jian-Lei Li Wei-Kang Shi |
| author_facet | Jian-Lei Li Wei-Kang Shi |
| author_sort | Jian-Lei Li |
| collection | DOAJ |
| description | In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis. |
| format | Article |
| id | doaj-art-7e7b1fb0dd0e41ceabc032edf35f60c4 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-7e7b1fb0dd0e41ceabc032edf35f60c42024-12-19T10:56:01ZengElsevierHeliyon2405-84402024-12-011024e40819Hybrid preprocessing for neural network-based stock price predictionJian-Lei Li0Wei-Kang Shi1Corresponding author.; North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR ChinaNorth China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR ChinaIn the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.http://www.sciencedirect.com/science/article/pii/S2405844024168508Empirical wavelet transform (EWT)Dynamic time warping (DTW)Principal component analysis (PCA)Neural networkStock price prediction |
| spellingShingle | Jian-Lei Li Wei-Kang Shi Hybrid preprocessing for neural network-based stock price prediction Heliyon Empirical wavelet transform (EWT) Dynamic time warping (DTW) Principal component analysis (PCA) Neural network Stock price prediction |
| title | Hybrid preprocessing for neural network-based stock price prediction |
| title_full | Hybrid preprocessing for neural network-based stock price prediction |
| title_fullStr | Hybrid preprocessing for neural network-based stock price prediction |
| title_full_unstemmed | Hybrid preprocessing for neural network-based stock price prediction |
| title_short | Hybrid preprocessing for neural network-based stock price prediction |
| title_sort | hybrid preprocessing for neural network based stock price prediction |
| topic | Empirical wavelet transform (EWT) Dynamic time warping (DTW) Principal component analysis (PCA) Neural network Stock price prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024168508 |
| work_keys_str_mv | AT jianleili hybridpreprocessingforneuralnetworkbasedstockpriceprediction AT weikangshi hybridpreprocessingforneuralnetworkbasedstockpriceprediction |