Outlier identification and adjustment for time series

Identification and replacement of erroneous data is of fundamental importance for the quality of statistical surveys. If statistical units are continuously sampled over an extended period, time series methods can facilitate this task. Numerous outlier identification and replacement procedures are accessible for this particular purpose, like RegArima Approaches within the seasonal adjustment procedures in X13-Arima or Tramo/Seats. These algorithms can be used to identify different types of outliers, like additive outliers, level shifts or transitory changes. In this paper an alternative outlier identification procedure is proposed which is based on a nonlinear model estimated with support vector regressions. The focus of this procedure is on the identification of additive outliers and on the applicability for short time series with less than 3 years of observations.