Predicting Chaos
The main advantage of detecting chaos is that the time series is short term predictable. The prediction accuracy decreases in time. A strong evidence of chaotic dynamics is the existence of a positive Lyapunov exponent (i.e. sensitivity to initial conditions). In chaotic time series prediction theor...
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| Format: | Article |
| Language: | English |
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Stefan cel Mare University of Suceava
2012-01-01
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| Series: | Journal of Applied Computer Science & Mathematics |
| Subjects: | |
| Online Access: | http://jacs.usv.ro/getpdf.php?paperid=13_12 |
| _version_ | 1818561348782522368 |
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| author | Sorin VLAD |
| author_facet | Sorin VLAD |
| author_sort | Sorin VLAD |
| collection | DOAJ |
| description | The main advantage of detecting chaos is that the time series is short term predictable. The prediction accuracy decreases in time. A strong evidence of chaotic dynamics is the existence of a positive Lyapunov exponent (i.e. sensitivity to initial conditions). In chaotic time series prediction theory the methods used can be placed in two classes: global and local methods. Neural networks are global methods of prediction. The paper tries to find a relation between the two parameters used in reconstruction of the state space (embedding dimension m and delay time τ) and the number of input neurons of a multilayer perceptron (MLP). For two of three time series studied, the minimum absolute error value is minimum for a MLP with the number of inputs equal to m*τ. |
| first_indexed | 2024-12-14T00:49:41Z |
| format | Article |
| id | doaj.art-a80e55fae3a04463b97955f570a86a80 |
| institution | Directory Open Access Journal |
| issn | 2066-4273 2066-3129 |
| language | English |
| last_indexed | 2024-12-14T00:49:41Z |
| publishDate | 2012-01-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Journal of Applied Computer Science & Mathematics |
| spelling | doaj.art-a80e55fae3a04463b97955f570a86a802022-12-21T23:23:55ZengStefan cel Mare University of SuceavaJournal of Applied Computer Science & Mathematics2066-42732066-31292012-01-016137982Predicting ChaosSorin VLADThe main advantage of detecting chaos is that the time series is short term predictable. The prediction accuracy decreases in time. A strong evidence of chaotic dynamics is the existence of a positive Lyapunov exponent (i.e. sensitivity to initial conditions). In chaotic time series prediction theory the methods used can be placed in two classes: global and local methods. Neural networks are global methods of prediction. The paper tries to find a relation between the two parameters used in reconstruction of the state space (embedding dimension m and delay time τ) and the number of input neurons of a multilayer perceptron (MLP). For two of three time series studied, the minimum absolute error value is minimum for a MLP with the number of inputs equal to m*τ.jacs.usv.ro/getpdf.php?paperid=13_12Chaos TheoryTime SeriesChaos IdentificationPrediction |
| spellingShingle | Sorin VLAD Predicting Chaos Journal of Applied Computer Science & Mathematics Chaos Theory Time Series Chaos Identification Prediction |
| title | Predicting Chaos |
| title_full | Predicting Chaos |
| title_fullStr | Predicting Chaos |
| title_full_unstemmed | Predicting Chaos |
| title_short | Predicting Chaos |
| title_sort | predicting chaos |
| topic | Chaos Theory Time Series Chaos Identification Prediction |
| url | http://jacs.usv.ro/getpdf.php?paperid=13_12 |
| work_keys_str_mv | AT sorinvlad predictingchaos |