Spectral modeling of time series with missing data |
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Authors: | Paulo C. Rodrigues Miguel de Carvalho |
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Affiliation: | 1. Center for Mathematics and Applications, Faculty of Sciences and Technology, Nova University of Lisbon, 2829-516 Caparica, Portugal;2. ISLA Campus Lisboa, Laureate International Universities, Lisbon, Portugal;3. Swiss Federal Institute of Technology, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland;4. Faculty of Mathematics, Pontificia Universidad Católica de Chile, Santiago, Chile |
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Abstract: | 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. |
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Keywords: | Karhunen&ndash Loè ve decomposition Missing data Singular spectrum analysis Time series analysis |
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