CNN-Trans-SPP: A small Transformer with CNN for stock price prediction
Understanding the patterns of financial activities and predicting their evolution and changes has always been a significant challenge in the field of behavioral finance. Stock price prediction is particularly difficult due to the inherent complexity and stochastic nature of the stock market. Deep le...
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AIMS Press
2024-12-01
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Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024314 |
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author | Ying Li Xiangrong Wang Yanhui Guo |
author_facet | Ying Li Xiangrong Wang Yanhui Guo |
author_sort | Ying Li |
collection | DOAJ |
description | Understanding the patterns of financial activities and predicting their evolution and changes has always been a significant challenge in the field of behavioral finance. Stock price prediction is particularly difficult due to the inherent complexity and stochastic nature of the stock market. Deep learning models offer a more robust solution to nonlinear problems compared to traditional algorithms. In this paper, we propose a simple yet effective fusion model that leverages the strengths of both transformers and convolutional neural networks (CNNs). The CNN component is employed to extract local features, while the Transformer component captures temporal dependencies. To validate the effectiveness of the proposed approach, we conducted experiments on four stocks representing different sectors, including finance, technology, industry, and agriculture. We performed both single-step and multi-step predictions. The experimental results demonstrate that our method significantly improves prediction accuracy, reducing error rates by 45%, 32%, and 36.8% compared to long short-term memory(LSTM), attention-based LSTM, and transformer models. |
format | Article |
id | doaj-art-2f0bac0f9fe1415db6776823c63f3e50 |
institution | Kabale University |
issn | 2688-1594 |
language | English |
publishDate | 2024-12-01 |
publisher | AIMS Press |
record_format | Article |
series | Electronic Research Archive |
spelling | doaj-art-2f0bac0f9fe1415db6776823c63f3e502025-01-23T07:53:06ZengAIMS PressElectronic Research Archive2688-15942024-12-0132126717673210.3934/era.2024314CNN-Trans-SPP: A small Transformer with CNN for stock price predictionYing Li0Xiangrong Wang1Yanhui Guo2College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Data and Computer Science, Shandong Women's University, Jinan 250300, ChinaUnderstanding the patterns of financial activities and predicting their evolution and changes has always been a significant challenge in the field of behavioral finance. Stock price prediction is particularly difficult due to the inherent complexity and stochastic nature of the stock market. Deep learning models offer a more robust solution to nonlinear problems compared to traditional algorithms. In this paper, we propose a simple yet effective fusion model that leverages the strengths of both transformers and convolutional neural networks (CNNs). The CNN component is employed to extract local features, while the Transformer component captures temporal dependencies. To validate the effectiveness of the proposed approach, we conducted experiments on four stocks representing different sectors, including finance, technology, industry, and agriculture. We performed both single-step and multi-step predictions. The experimental results demonstrate that our method significantly improves prediction accuracy, reducing error rates by 45%, 32%, and 36.8% compared to long short-term memory(LSTM), attention-based LSTM, and transformer models.https://www.aimspress.com/article/doi/10.3934/era.2024314stock price predictiontransformercnnlstm |
spellingShingle | Ying Li Xiangrong Wang Yanhui Guo CNN-Trans-SPP: A small Transformer with CNN for stock price prediction Electronic Research Archive stock price prediction transformer cnn lstm |
title | CNN-Trans-SPP: A small Transformer with CNN for stock price prediction |
title_full | CNN-Trans-SPP: A small Transformer with CNN for stock price prediction |
title_fullStr | CNN-Trans-SPP: A small Transformer with CNN for stock price prediction |
title_full_unstemmed | CNN-Trans-SPP: A small Transformer with CNN for stock price prediction |
title_short | CNN-Trans-SPP: A small Transformer with CNN for stock price prediction |
title_sort | cnn trans spp a small transformer with cnn for stock price prediction |
topic | stock price prediction transformer cnn lstm |
url | https://www.aimspress.com/article/doi/10.3934/era.2024314 |
work_keys_str_mv | AT yingli cnntranssppasmalltransformerwithcnnforstockpriceprediction AT xiangrongwang cnntranssppasmalltransformerwithcnnforstockpriceprediction AT yanhuiguo cnntranssppasmalltransformerwithcnnforstockpriceprediction |