Application of Neural Networks in Financial Time Series Forecasting Models
At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2022/7817264 |
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| _version_ | 1832562210961883136 |
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| author | Xinhui Li |
| author_facet | Xinhui Li |
| author_sort | Xinhui Li |
| collection | DOAJ |
| description | At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index. |
| format | Article |
| id | doaj-art-b1603c10e63d450b818c3aa930765eb7 |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-b1603c10e63d450b818c3aa930765eb72025-02-03T01:23:12ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/7817264Application of Neural Networks in Financial Time Series Forecasting ModelsXinhui Li0Department of Applied EconomicsAt present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index.http://dx.doi.org/10.1155/2022/7817264 |
| spellingShingle | Xinhui Li Application of Neural Networks in Financial Time Series Forecasting Models Journal of Function Spaces |
| title | Application of Neural Networks in Financial Time Series Forecasting Models |
| title_full | Application of Neural Networks in Financial Time Series Forecasting Models |
| title_fullStr | Application of Neural Networks in Financial Time Series Forecasting Models |
| title_full_unstemmed | Application of Neural Networks in Financial Time Series Forecasting Models |
| title_short | Application of Neural Networks in Financial Time Series Forecasting Models |
| title_sort | application of neural networks in financial time series forecasting models |
| url | http://dx.doi.org/10.1155/2022/7817264 |
| work_keys_str_mv | AT xinhuili applicationofneuralnetworksinfinancialtimeseriesforecastingmodels |