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キョウ コウキ
Kyo Koki
姜 興起 所属 デジタルトランスフォーメーション（DX）推進センター 職種 教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2024/03/08 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | An approach for the identification and estimation of outliers in a time series with a nonstationary mean |
| 執筆形態 | 単著 |
| 掲載誌名 | Proceedings of the 2023 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'23) |
| 掲載区分 | 国外 |
| 出版社・発行元 | IEEE Conference Publishing Services |
| 巻・号・頁 | pp.1477-1482 |
| 総ページ数 | 6 |
| 担当範囲 | 論文原稿の主要な部分 |
| 著者・共著者 | Koki Kyo |
| 概要 | We address the problem of identifying and estimating outliers in a time series with a nonstationary mean. We first apply a moving linear model to decompose the time series into a constrained component and a remaining component that is a stationary time series containing the outliers. We then propose an approach of identifying and estimating the outliers. The proposed approach is based on the maximum likelihood method for an autoregression (AR) model with outliers in variables and has the following features. (1) The used model has a simple structure
that is easy to understand. (2) The proposed approach allows for the estimation of not only the positions but also the magnitudes of the outliers. As an illustrative example, we apply the proposed approach to the analysis of the index of industrial production in Japan. The results show the high performance of the approach. |