Imputation Algorithm Based on Copula for Missing Value in Timeseries Data

被引：0

作者：

Afrianti, Y. S. ^{[1
]}

Indratno, S. W. ^{[1
]}

Pasaribu, U. S. ^{[1
]}

机构：

[1] Inst Teknol Bandung, Stat Res Grp, Fac Math & Nat Sci, Bandung, Indonesia

来源：

2014 2ND INTERNATIONAL CONFERENCE ON TECHNOLOGY, INFORMATICS, MANAGEMENT, ENGINEERING, AND ENVIRONMENT (TIME-E 2014) | 2014年

关键词：

imputation; gaussian copula; time series; missing value; conditional distribution;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, imputation algorithm based on Gaussian copula in time series data is given. The case study is a missing value of 33 years Gross Development Product (GDP) of nine countries (from 1950 to 1983). The missing value was predicted by error model of autoregressive (AR) model assumed following N(mu, sigma(2)) distribution. Since the data is time series and modeled with AR, the recent data is influenced by previous data, added coefficient factor and error. Thus conditional distribution of the measurement at specific time point, which is also conditioned by past measurements, was analyzed. In this research, the conditional distribution, so called joint distribution, was derived by copula. The result shows that the proposed method could predict the missing value with small error.

引用

页码：252 / 257

页数：6

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