Paper Abstract and Keywords |
Presentation |
2019-07-30 10:45
Modeling and Prediction of Time Time-Series
-- A Case Study with Forex Data -- Shiao Yi Chen, Goutam Chakraborty, Chen Shin Fu (IPU) CAS2019-2 VLD2019-8 SIP2019-18 MSS2019-2 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Time series data reveals dynamic behavior of systems in real life, such as traffic flow, amount of rainfall, usage of electricity, share values, Forex rate etc.. Depending on the complexity of the system dynamics, algorithms differ to model the time series data accurately, so that the model can be used for interpolation and more commonly extrapolation or prediction. For example, AR model performs well in stationary time seri es, but for non stationary, it cannot capture the pattern well. In this research, we use Forex rate data, and tried various algorithms to capture the dynamics of the data. The success of the model is evaluated by accuracy in prediction.
In our experiments, we applied two state of the art models Support Vector Regression (SVR) and Recurrent Neural Network (RNN). The experiment is the prediction of longer future by recursion (feeding back predicted value to input for the next step prediction). The result shows that RNN with proper Long Short Term Memory (LSTM) has better performance in predicting longer future. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Time series / Forex / SVR / RNN / LSTM / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 155, SIP2019-18, pp. 7-10, July 2019. |
Paper # |
SIP2019-18 |
Date of Issue |
2019-07-23 (CAS, VLD, SIP, MSS) |
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) |
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CAS2019-2 VLD2019-8 SIP2019-18 MSS2019-2 |
Conference Information |
Committee |
MSS CAS SIP VLD |
Conference Date |
2019-07-30 - 2019-07-31 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Iwate Univ. |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SIP |
Conference Code |
2019-07-MSS-CAS-SIP-VLD |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Modeling and Prediction of Time Time-Series |
Sub Title (in English) |
A Case Study with Forex Data |
Keyword(1) |
Time series |
Keyword(2) |
Forex |
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SVR |
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RNN |
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LSTM |
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1st Author's Name |
Shiao Yi Chen |
1st Author's Affiliation |
Iwate Prefectural University (IPU) |
2nd Author's Name |
Goutam Chakraborty |
2nd Author's Affiliation |
Iwate Prefectural University (IPU) |
3rd Author's Name |
Chen Shin Fu |
3rd Author's Affiliation |
Iwate Prefectural University (IPU) |
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Speaker |
Author-1 |
Date Time |
2019-07-30 10:45:00 |
Presentation Time |
25 minutes |
Registration for |
SIP |
Paper # |
CAS2019-2, VLD2019-8, SIP2019-18, MSS2019-2 |
Volume (vol) |
vol.119 |
Number (no) |
no.153(CAS), no.154(VLD), no.155(SIP), no.156(MSS) |
Page |
pp.7-10 |
#Pages |
4 |
Date of Issue |
2019-07-23 (CAS, VLD, SIP, MSS) |
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