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| Presentation | 2020-03-10 13:30 Influence of Resolution of Time Series Data on Causality Detection Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS)  NLP2019-128 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this report, we investigated the influence of resolution of time series data on causality detection by Convergent Cross Mapping~(CCM). First, we transformed original time series data generated from a coupled logistic map to low-resolution integer time series data. Then, we performed causality detection for the low-resolution integer time series data. We found that although it is more difficult to detect a causal relationship for the low-resolution integer time series data, it is possible detect a causal relationship with a resolution of about $2^3$ when there is a bidirectional causal relationship. In addition, our results suggest that it is more difficult to detect the non-causality than to detect the causality when we have low-resolution data. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Nonlinear Time Series Analysis / Causality / Coupled Dynamical System / Data Resolution / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 471, NLP2019-128, pp. 89-94, March 2020. | |
| Paper # | NLP2019-128 | |
| Date of Issue | 2020-03-02 (NLP) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NLP2019-128 |
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| Conference Information | |
| Committee | MSS NLP |
| Conference Date | 2020-03-09 - 2020-03-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | SICE-DES, IEICE-MSS, IEICE-NLP |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2020-03-MSS-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Influence of Resolution of Time Series Data on Causality Detection |
| Sub Title (in English) | |
| Keyword(1) | Nonlinear Time Series Analysis |
| Keyword(2) | Causality |
| Keyword(3) | Coupled Dynamical System |
| Keyword(4) | Data Resolution |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Kazuya Sawada |
| 1st Author's Affiliation | Tokyo University of Science (TUS) |
| 2nd Author's Name | Yutaka Shimada |
| 2nd Author's Affiliation | Saitama University (Saitama Univ.) |
| 3rd Author's Name | Tohru Ikeguchi |
| 3rd Author's Affiliation | Tokyo University of Science (TUS) |
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| Speaker | Author-1 |
| Date Time | 2020-03-10 13:30:00 |
| Presentation Time | 25 minutes |
| Registration for | NLP |
| Paper # | NLP2019-128 |
| Volume (vol) | vol.119 |
| Number (no) | no.471 |
| Page | pp.89-94 |
| #Pages | 6 |
| Date of Issue | 2020-03-02 (NLP) |
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