ABOUT A PROMPT STRATEGY FOR ESTIMATING MISSING DATA IN LONG TIME SERIES
Vol. 26 No. 100 (2002), Mathematics
Vol. 26 No. 100 (2002)

ABOUT A PROMPT STRATEGY FOR ESTIMATING MISSING DATA IN LONG TIME SERIES

Mathematics
https://doi.org/10.18257/raccefyn.26(100).2002.2687
Published 2024-08-03
Fabio H. Nieto
Departamento de Estadística, Universidad Nacional de Colombia, Bogotá
Fernando Ruiz
IDEAM, Bogotá, Colombia.
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How to Cite

H. Nieto, F., & Ruiz, F. (2024). ABOUT A PROMPT STRATEGY FOR ESTIMATING MISSING DATA IN LONG TIME SERIES. Revista De La Academia Colombiana De Ciencias Exactas, Físicas Y Naturales, 26(100), 411–418. https://doi.org/10.18257/raccefyn.26(100).2002.2687
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Abstract

A quick practical strategy is proposed for estimating missing data in time series that obey low-order ARIMA models and whose length is greater than that supported by current statistical computer programs. The proposed methodology is based on the idea of identifying the series model from its subseries. To obtain these subseries, a minimal number of data points following a missing observation are deduced to achieve numerical stabilization in its recursive prediction.

https://doi.org/10.18257/raccefyn.26(100).2002.2687

Keywords

ARIMA Models | Missing Observations | Model Identification
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References

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