Published

2007-07-01

RECONSTRUCCIÓN DE DATOS DE SERIES DE TIEMPO: UNA APLICACIÓN A LA DEMANDA HORARIA DE LA ELECTRICIDAD

TIME SERIES DATA RECONSTRUCTION: AN APPLICATION TO THE HOURLY DEMAND OF ELECTRICITY

Keywords:

observaciones atípicas, observaciones faltantes, intervención, función de transferencia, ARIMA (es)
Atypical observations, Missing observations, Intervention, Transfer function, ARIMA (en)

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Authors

  • Elkin Castaño Universidad Nacional de Colombia Sede Medellín / Universidad de Antioquia
Generalmente, la identificación y estimación de modelos ARIMA parten del supuesto de que las series que se van a analizar no contienen datos faltantes, ni observaciones atípicas, ni existen intervenciones en el período de estudio. Sin embargo, en la práctica, estos problemas pueden ocurrir simultáneamente, afectando la identificación del modelo adecuado y por tanto su capacidad de pronóstico. Este artículo presenta un procedimiento que permite estimar el efecto de las intervenciones, de las observaciones atípicas, estimar las observaciones faltantes y simultáneamente identificar el modelo ARIMA. El procedimiento se aplica a una serie de demanda horaria de electricidad en la cual ocurren los tres eventos mencionados.
Usually, in the identification and estimation of ARIMA models it is supposed that the series to analyze contain neither missing data, nor atypical observations, and interventions do not exist under study period. Nevertheless, in the practice, these problems can happen simultaneously, affecting the identification of the suitable model and therefore his forecasting capacity. This article presents a procedure that allows to estimate the effect of the interventions, of the atypical observations, to estimate the missing observations and simultaneously to identify the ARIMA model. The procedure is applied to a series of hourly electricity demand in which the three mentioned events happen.

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Copyright (c) 2007 Revista Colombiana de Estadística

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