On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series
Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/5/584 |
| _version_ | 1797567314390941696 |
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| author | Riccardo Rossi Andrea Murari Pasquale Gaudio |
| author_facet | Riccardo Rossi Andrea Murari Pasquale Gaudio |
| author_sort | Riccardo Rossi |
| collection | DOAJ |
| description | Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series. |
| first_indexed | 2024-03-10T19:40:58Z |
| format | Article |
| id | doaj.art-be002573efa24309bfae42faaa751bd0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T19:40:58Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-be002573efa24309bfae42faaa751bd02023-11-20T01:18:36ZengMDPI AGEntropy1099-43002020-05-0122558410.3390/e22050584On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time SeriesRiccardo Rossi0Andrea Murari1Pasquale Gaudio2Department of Industrial Engineering, University of Rome “Tor Vergata”, via del Politecnico 1, 00100 Roma, ItalyConsorzio RFX (CNR, ENEA, INFN, Universita di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova, ItalyDepartment of Industrial Engineering, University of Rome “Tor Vergata”, via del Politecnico 1, 00100 Roma, ItalyDetermining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series.https://www.mdpi.com/1099-4300/22/5/584time seriesindirect couplingtime delay neural networksLorenz system |
| spellingShingle | Riccardo Rossi Andrea Murari Pasquale Gaudio On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series Entropy time series indirect coupling time delay neural networks Lorenz system |
| title | On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series |
| title_full | On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series |
| title_fullStr | On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series |
| title_full_unstemmed | On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series |
| title_short | On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series |
| title_sort | on the potential of time delay neural networks to detect indirect coupling between time series |
| topic | time series indirect coupling time delay neural networks Lorenz system |
| url | https://www.mdpi.com/1099-4300/22/5/584 |
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