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...
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
References and Further Reading
Abraham B, Chuang C (1989) Outlier detection and time series modeling. Technometrics 31(2):241–247
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):221–234
Box GEP, Jenkins GM (1970, 1976) Time series analysis forecasting and control. Holden Day, San Francisco
Box GEP, Tiao GC (1975) Intervention analysis with application to economic and environmental problems. J Am Stat Assoc 70(34):70–79
Chang I, Tiao GC, Chen C (1988) Estimation of time series parameters in the presence of outliers. Technometrics 30(2):193–204
Chareka P, Matarise F, Turner R (2006) A test for additive outliers applicable to long memory time series. J Econ Dyn Control 30(6):595–621
Chen C, Liu L-M (1993) Joint estimation of model parameter and outlier effects in time series. J Am Stat Assoc 88:284–297
Kirkendall N (1992) Monitoring outliers and level shifts in Kalman filter implementations of exponential smoothing. J Forecasting 11:543–550
Editors and Affiliations
© 2011 Springer-Verlag Berlin Heidelberg
About this entry
Cite this entry
Matarise, F. (2011). Intervention Analysis in Time Series. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_308
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04897-5
Online ISBN: 978-3-642-04898-2