Processing of Missing Observations and Outliers in Time Series

  • Genshiro Kitagawa
Part of the Statistics for Engineering and Physical Science book series (ISS)


Thanks to rapid and remarkable development of electronic devices such as computers and measurement instruments, a variety of time series has been available automatically and continuously, resulting in minute-by-minute accumulation of a large amount of data. However when time series is observed for a long period, part of the time series is sometimes unable to be observed owing to some accidental causes such as malfunction of instrument or some physical restrictions of the observation system. In such a case, unobserved data is called missing observations. Even with only several percent of the missing observations, if they are studded with the data, the length of actually available data may become very short if only a part observed continuously is taken out.


Kalman Filter Time Series Model Variance Covariance Matrix Ground Water Level Observation Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York, Inc. 1999

Authors and Affiliations

  • Genshiro Kitagawa
    • 1
  1. 1.The Institute of Statistical MathematicsTokyoJapan

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