Time series interpolation via global optimization of moments fitting

Most time series forecasting methods assume the series has no missing values. When missing values exist, interpolation methods, while filling in the blanks, may substantially modify the statistical pattern of the data, since critical features such as moments and autocorrelations are not necessarily preserved.

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Year of publication:	2013
Authors:	Carrizosa, Emilio ; Olivares-Nadal, Alba V. ; Ramírez-Cobo, Pepa
Published in:	European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 230.2013, 1, p. 97-112
Publisher:	Elsevier
Subject:	Missing values \| Moments matching \| Global optimization \| Variable Neighborhood Search

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011052705