Combining market-guided patterns and mamba for stock price prediction
Stock prices prediction is a highly challenging task over many years, owing to the market’s high volatility. With the development of deep learning, various studies has focused on modeling temporal patterns for stock price prediction. Most existing approaches rely on a shared neural architecture that...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206961773215744 |
|---|---|
| author | Yanshuo Chang Wei Lu Feng Xue Xinyu Lu |
| author_facet | Yanshuo Chang Wei Lu Feng Xue Xinyu Lu |
| author_sort | Yanshuo Chang |
| collection | DOAJ |
| description | Stock prices prediction is a highly challenging task over many years, owing to the market’s high volatility. With the development of deep learning, various studies has focused on modeling temporal patterns for stock price prediction. Most existing approaches rely on a shared neural architecture that captures temporal patterns from individual stock series and then combines these temporal representations to form stock correlations. To overcome the above-mentioned problems, a novel market-embedding with Mamba (MEM) architecture, is proposed. Specifically, MEM consists of the following architectures, coarse-grained feature aggregation, fine-grained feature aggregation, temporal feature aggregation, which can extract the discriminative patterns for stock price prediction. The various experimental results show that the proposed method MEM surpasses previous approaches on two publicly available datasets, i.e., CSI300 and CSI800. |
| format | Article |
| id | doaj-art-8eaedb44178f44a490d2a0a365a665b8 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-8eaedb44178f44a490d2a0a365a665b82025-02-07T04:46:57ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113287293Combining market-guided patterns and mamba for stock price predictionYanshuo Chang0Wei Lu1Feng Xue2Xinyu Lu3Corresponding author.; School of Information, Xi’an University of Finance and Economics, Xi’an 710100, ChinaSchool of Information, Xi’an University of Finance and Economics, Xi’an 710100, ChinaSchool of Information, Xi’an University of Finance and Economics, Xi’an 710100, ChinaSchool of Information, Xi’an University of Finance and Economics, Xi’an 710100, ChinaStock prices prediction is a highly challenging task over many years, owing to the market’s high volatility. With the development of deep learning, various studies has focused on modeling temporal patterns for stock price prediction. Most existing approaches rely on a shared neural architecture that captures temporal patterns from individual stock series and then combines these temporal representations to form stock correlations. To overcome the above-mentioned problems, a novel market-embedding with Mamba (MEM) architecture, is proposed. Specifically, MEM consists of the following architectures, coarse-grained feature aggregation, fine-grained feature aggregation, temporal feature aggregation, which can extract the discriminative patterns for stock price prediction. The various experimental results show that the proposed method MEM surpasses previous approaches on two publicly available datasets, i.e., CSI300 and CSI800.http://www.sciencedirect.com/science/article/pii/S1110016824012821Stock price predictionMambaDeep learningMarket patterns |
| spellingShingle | Yanshuo Chang Wei Lu Feng Xue Xinyu Lu Combining market-guided patterns and mamba for stock price prediction Alexandria Engineering Journal Stock price prediction Mamba Deep learning Market patterns |
| title | Combining market-guided patterns and mamba for stock price prediction |
| title_full | Combining market-guided patterns and mamba for stock price prediction |
| title_fullStr | Combining market-guided patterns and mamba for stock price prediction |
| title_full_unstemmed | Combining market-guided patterns and mamba for stock price prediction |
| title_short | Combining market-guided patterns and mamba for stock price prediction |
| title_sort | combining market guided patterns and mamba for stock price prediction |
| topic | Stock price prediction Mamba Deep learning Market patterns |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824012821 |
| work_keys_str_mv | AT yanshuochang combiningmarketguidedpatternsandmambaforstockpriceprediction AT weilu combiningmarketguidedpatternsandmambaforstockpriceprediction AT fengxue combiningmarketguidedpatternsandmambaforstockpriceprediction AT xinyulu combiningmarketguidedpatternsandmambaforstockpriceprediction |