A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices
The accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. I...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/7119678 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849683099989835776 |
|---|---|
| author | Zong-Yu Peng Pei-Chang Guo |
| author_facet | Zong-Yu Peng Pei-Chang Guo |
| author_sort | Zong-Yu Peng |
| collection | DOAJ |
| description | The accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. It is shown that organizing the input data can help get accurate outcomes of the models. In this paper, we first introduce some basic knowledge about LSTM and present prediction results using a standard LSTM model. Then, we show how to organize the input data during the training period and give the comparison results for not only LSTM but also the transformer model. The numerical results show that the prediction results of LSTM and transformer can be improved after the input data are organized when training. |
| format | Article |
| id | doaj-art-ca28bc52d73642bc9a131f02650ad44c |
| institution | DOAJ |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-ca28bc52d73642bc9a131f02650ad44c2025-08-20T03:23:59ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/7119678A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock PricesZong-Yu Peng0Pei-Chang Guo1School of ScienceSchool of ScienceThe accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. It is shown that organizing the input data can help get accurate outcomes of the models. In this paper, we first introduce some basic knowledge about LSTM and present prediction results using a standard LSTM model. Then, we show how to organize the input data during the training period and give the comparison results for not only LSTM but also the transformer model. The numerical results show that the prediction results of LSTM and transformer can be improved after the input data are organized when training.http://dx.doi.org/10.1155/2022/7119678 |
| spellingShingle | Zong-Yu Peng Pei-Chang Guo A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices Discrete Dynamics in Nature and Society |
| title | A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices |
| title_full | A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices |
| title_fullStr | A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices |
| title_full_unstemmed | A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices |
| title_short | A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices |
| title_sort | data organization method for lstm and transformer when predicting chinese banking stock prices |
| url | http://dx.doi.org/10.1155/2022/7119678 |
| work_keys_str_mv | AT zongyupeng adataorganizationmethodforlstmandtransformerwhenpredictingchinesebankingstockprices AT peichangguo adataorganizationmethodforlstmandtransformerwhenpredictingchinesebankingstockprices AT zongyupeng dataorganizationmethodforlstmandtransformerwhenpredictingchinesebankingstockprices AT peichangguo dataorganizationmethodforlstmandtransformerwhenpredictingchinesebankingstockprices |