Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network
In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/5/823 |
| _version_ | 1797567797673328640 |
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| author | Tianle Zhou Chaoyi Chu Chaobin Xu Weihao Liu Hao Yu |
| author_facet | Tianle Zhou Chaoyi Chu Chaobin Xu Weihao Liu Hao Yu |
| author_sort | Tianle Zhou |
| collection | DOAJ |
| description | In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data. |
| first_indexed | 2024-03-10T19:47:34Z |
| format | Article |
| id | doaj.art-5c3725e02e2d4e10bc52093d661087b4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T19:47:34Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5c3725e02e2d4e10bc52093d661087b42023-11-20T00:41:50ZengMDPI AGElectronics2079-92922020-05-019582310.3390/electronics9050823Detecting Predictable Segments of Chaotic Financial Time Series via Neural NetworkTianle Zhou0Chaoyi Chu1Chaobin Xu2Weihao Liu3Hao Yu4School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaIn this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.https://www.mdpi.com/2079-9292/9/5/823financial time serieschaotic dynamicsSOM neural networkclusteringLSTM |
| spellingShingle | Tianle Zhou Chaoyi Chu Chaobin Xu Weihao Liu Hao Yu Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network Electronics financial time series chaotic dynamics SOM neural network clustering LSTM |
| title | Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network |
| title_full | Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network |
| title_fullStr | Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network |
| title_full_unstemmed | Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network |
| title_short | Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network |
| title_sort | detecting predictable segments of chaotic financial time series via neural network |
| topic | financial time series chaotic dynamics SOM neural network clustering LSTM |
| url | https://www.mdpi.com/2079-9292/9/5/823 |
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