WEKO3
アイテム
Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series
http://hdl.handle.net/10228/00006936
http://hdl.handle.net/10228/00006936315aab47-b6a1-41f7-a925-935e7d38501e
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
ssci17timeseries.pdf (2.1 MB)
|
|
| Item type | 会議発表論文 = Conference Paper(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2018-10-02 | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
| タイトル | ||||||||||||
| タイトル | Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 著者 |
黒木, 秀一
× 黒木, 秀一× Shimoda, Naoto× 松尾, 一矢
WEKO
15851
|
|||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to the long term unpredictability of chaotic time series. Thus, we have developed a method to select a representative prediction from a set of plausible predictions by means of using LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. Although we have shown the effectiveness of the method, it sometimes fails to select the representative prediction with long predictable horizon. In order to cope with this problem, this paper presents a method to select multiple candidates of representative prediction by means of employing hierarchical K-means clustering with K = 2. From numerical experiments, we show the effectiveness of the method and an analysis of the property of LOOCV predictable horizon. | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, USA | |||||||||||
| 書誌情報 |
2017 IEEE Symposium Series on Computational Intelligence (SSCI) 発行日 2017-12-01 |
|||||||||||
| 出版社 | ||||||||||||
| 出版社 | IEEE | |||||||||||
| DOI | ||||||||||||
| 関連タイプ | isVersionOf | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1109/SSCI.2017.8285285 | |||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | hierarchical clustering of predictions | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | ensemble prediction of chaotic time series | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | leave-one-out predictable horizon | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | long-term unpredictability | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | AM | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||
| 査読の有無 | ||||||||||||
| 値 | yes | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/12_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10324508 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 7183 | |||||||||||
