Spectral modeling of time series with missing data

被引：24
|
作者：
Rodrigues, Paulo C. [1 ,2 ]
de Carvalho, Miguel [1 ,3 ,4 ]
机构：
[1] Nova Univ Lisbon, Ctr Math & Applicat, Fac Sci & Technol, P-2829516 Caparica, Portugal
[2] Laureate Int Univ, ISLA Campus Lisboa, Lisbon, Portugal
[3] Ecole Polytech Fed Lausanne, Swiss Fed Inst Technol, CH-1015 Lausanne, Switzerland
[4] Pontificia Univ Catolica Chile, Fac Math, Santiago, Chile
来源：
APPLIED MATHEMATICAL MODELLING | 2013年 / 37卷 / 07期
关键词：
Karhunen-Loeve decomposition; Missing data; Singular spectrum analysis; Time series analysis; DYNAMICS; SSA;
D O I：
10.1016/j.apm.2012.09.040
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the forecasts and hindcasts yield by the recurrent forecast method. Despite its ease of implementation, the obtained results suggest an overall good fit of our method, being able to yield a similar adjustment ability in comparison with the alternative method, according to some measures of predictive performance. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码：4676 / 4684
页数：9
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