International Encyclopedia of Statistical Science

2011 Edition
| Editors: Miodrag Lovric

Intervention Analysis in Time Series

  • Florance Matarise
Reference work entry

Intervention analysis is the application of modeling procedures for incorporating the effects of exogenous forces or interventions in time series analysis. These interventions, like policy changes, strikes, floods, and price changes, cause unusual changes in time series, resulting in unexpected, extraordinary observations known as  outliers. Specifically, four types of outliers resulting from interventions, additive outliers (AO), innovational outliers (IO), temporary changes (TC), and level shifts (LS), have generated a lot of interest in literature. They pose nonstationarity challenges, which cannot be represented by the usual Box and Jenkins (1976) autoregressive integrated moving average (ARIMA) models alone.

The most popular modeling procedures are those where “intervention” detection and estimation is paramount. Box and Tiao (1975) pioneered this type of analysis in their quest to solve the Los Angeles pollution problem. Important extensions and contributions have been made by...

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References and Further Reading

  1. Abraham B, Chuang C (1989) Outlier detection and time series modeling. Technometrics 31(2):241–247zbMATHMathSciNetGoogle Scholar
  2. Abraham B, Chuang C (1993) Expectation-maximization algorithms and the estimation of time series models in the presence of outliers. J Time Ser Anal 14(3):221–234zbMATHMathSciNetGoogle Scholar
  3. Box GEP, Jenkins GM (1970, 1976) Time series analysis forecasting and control. Holden Day, San FranciscozbMATHGoogle Scholar
  4. Box GEP, Tiao GC (1975) Intervention analysis with application to economic and environmental problems. J Am Stat Assoc 70(34):70–79zbMATHMathSciNetGoogle Scholar
  5. Chang I, Tiao GC, Chen C (1988) Estimation of time series parameters in the presence of outliers. Technometrics 30(2):193–204MathSciNetGoogle Scholar
  6. Chareka P, Matarise F, Turner R (2006) A test for additive outliers applicable to long memory time series. J Econ Dyn Control 30(6):595–621zbMATHMathSciNetGoogle Scholar
  7. Chen C, Liu L-M (1993) Joint estimation of model parameter and outlier effects in time series. J Am Stat Assoc 88:284–297zbMATHGoogle Scholar
  8. Kirkendall N (1992) Monitoring outliers and level shifts in Kalman filter implementations of exponential smoothing. J Forecasting 11:543–550Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Florance Matarise
    • 1
  1. 1.University of ZimbabweHarereZimbabwe