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| Presentation | 2021-12-16 15:15 Multivariate time series forecasting accuracy improvement method based on LSTNet Hayato Sano, Jun Rokui (Univ of Shizuoka)  PRMU2021-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Multivariate time series forecasting is a field to predict future values by analyzing the past of multiple time series data, and various methods have been proposed.In this study, two techniques with improved Long and Short term Time series Network(LSTNet) are proposed. LSTNet has a problem that long-term forecasts cannot be made for data with large scale changes. Therefore, Multiple Autoregressive LSTNet (MALSTNet) is proposed as a model with plural autoregressive layers. In addition, Gated recurrent unit (GRUs) used in Recurrent layers refer to historical data uniformly. It is unlikely that all historical information has an impact on forecasting uniformly, and Attention-LSTNet(ALSTNet) is proposed as a model that emphasizes certain historical interval information. In this study, we verified the effectiveness of the two methods from verification experiments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multivariate Time Series Forecasting / Autoregressive / Long and Short term Time series Network / LSTNet / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-37, pp. 71-76, Dec. 2021. | |
| Paper # | PRMU2021-37 | |
| Date of Issue | 2021-12-09 (PRMU) | |
| 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) | |
| Download PDF | PRMU2021-37 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2021-12-16 - 2021-12-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Multivariate time series forecasting accuracy improvement method based on LSTNet |
| Sub Title (in English) | |
| Keyword(1) | Multivariate Time Series Forecasting |
| Keyword(2) | Autoregressive |
| Keyword(3) | Long and Short term Time series Network |
| Keyword(4) | LSTNet |
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| Keyword(8) | |
| 1st Author's Name | Hayato Sano |
| 1st Author's Affiliation | University of Shizuoka (Univ of Shizuoka) |
| 2nd Author's Name | Jun Rokui |
| 2nd Author's Affiliation | University of Shizuoka (Univ of Shizuoka) |
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| Speaker | Author-1 |
| Date Time | 2021-12-16 15:15:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-37 |
| Volume (vol) | vol.121 |
| Number (no) | no.304 |
| Page | pp.71-76 |
| #Pages | 6 |
| Date of Issue | 2021-12-09 (PRMU) |
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