Paper Abstract and Keywords |
Presentation |
2023-03-16 10:00
Detecting causality for marked point processes Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) MSS2022-80 NLP2022-125 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this report, by modifying conventional causality detection method for nonlinear dynamical systems, we propose a causality detection method for marked point processes based on the embedding theorem. In the proposed method, we mutually predict the time series of the marked values and the inter-event intervals (IEIs) obtained from the marked point process, and detect causality based on the prediction accuracies. The proposed method is composed of reconstructing the dynamical system from the two-dimensional time series generated by the time series of the marked values and IEIs, and of performing temporal matching of the indices by using the event occurrence timing on the marked point process. We verified the effectiveness of the proposed method by applying it to the marked point process obtained by extracting the local maxima of the time series generated by the coupled Lorenz systems. The results suggest that the proposed method can accurately detect causality in all cases of bidirectional coupling, unidirectional coupling, and non-coupling. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
reconstructing dynamical system / embedding theorem / delay-coordinate system / marked point process / detecting causality / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 436, NLP2022-125, pp. 89-94, March 2023. |
Paper # |
NLP2022-125 |
Date of Issue |
2023-03-08 (MSS, NLP) |
ISSN |
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) |
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MSS2022-80 NLP2022-125 |
Conference Information |
Committee |
NLP MSS |
Conference Date |
2023-03-15 - 2023-03-17 |
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(See Japanese page) |
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Paper Information |
Registration To |
NLP |
Conference Code |
2023-03-NLP-MSS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Detecting causality for marked point processes |
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Keyword(1) |
reconstructing dynamical system |
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embedding theorem |
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delay-coordinate system |
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marked point process |
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detecting causality |
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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.) |
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Tohru Ikeguchi |
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Tokyo University of Science (TUS) |
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Speaker |
Author-1 |
Date Time |
2023-03-16 10:00:00 |
Presentation Time |
20 minutes |
Registration for |
NLP |
Paper # |
MSS2022-80, NLP2022-125 |
Volume (vol) |
vol.122 |
Number (no) |
no.435(MSS), no.436(NLP) |
Page |
pp.89-94 |
#Pages |
6 |
Date of Issue |
2023-03-08 (MSS, NLP) |
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