Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction
We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/10/1124 |
| _version_ | 1797551851217879040 |
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| author | Payam Shahsavari Baboukani Carina Graversen Emina Alickovic Jan Østergaard |
| author_facet | Payam Shahsavari Baboukani Carina Graversen Emina Alickovic Jan Østergaard |
| author_sort | Payam Shahsavari Baboukani |
| collection | DOAJ |
| description | We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data. |
| first_indexed | 2024-03-10T15:51:59Z |
| format | Article |
| id | doaj.art-a8ee3acd3f2242d896ae749a5e9f7111 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T15:51:59Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a8ee3acd3f2242d896ae749a5e9f71112023-11-20T15:59:33ZengMDPI AGEntropy1099-43002020-10-012210112410.3390/e22101124Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear PredictionPayam Shahsavari Baboukani0Carina Graversen1Emina Alickovic2Jan Østergaard3Department of Electronic Systems, Aalborg University, 9220 Aalborg, DenmarkEriksholm Research Centre, Oticon A/S, 3070 Snekkersten, DenmarkEriksholm Research Centre, Oticon A/S, 3070 Snekkersten, DenmarkDepartment of Electronic Systems, Aalborg University, 9220 Aalborg, DenmarkWe propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.https://www.mdpi.com/1099-4300/22/10/1124directed dependencyconditional transfer entropynon-uniform embeddingnonlinear predictionmutual information |
| spellingShingle | Payam Shahsavari Baboukani Carina Graversen Emina Alickovic Jan Østergaard Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction Entropy directed dependency conditional transfer entropy non-uniform embedding nonlinear prediction mutual information |
| title | Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction |
| title_full | Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction |
| title_fullStr | Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction |
| title_full_unstemmed | Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction |
| title_short | Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction |
| title_sort | estimating conditional transfer entropy in time series using mutual information and nonlinear prediction |
| topic | directed dependency conditional transfer entropy non-uniform embedding nonlinear prediction mutual information |
| url | https://www.mdpi.com/1099-4300/22/10/1124 |
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