Intervention Analysis in Time Series
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...
References and Further Reading
- Kirkendall N (1992) Monitoring outliers and level shifts in Kalman filter implementations of exponential smoothing. J Forecasting 11:543–550Google Scholar