论文信息 - Sequential Procedures for Detecting Parameter Changes in a Time-Series Model

Sequential Procedures for Detecting Parameter Changes in a Time-Series Model

Abstract Procedures are proposed for monitoring forecast errors in order to detect changes in a time-series model. These procedures are based on likelihood ratio statistics which consist of cumulative sums. An extension of Page's method is presented which tests for changes in the parameter values of autoregressive integrated moving average (arima) models. The distributional properties of the statistics are approximated under the assumption that the series follows an integrated autoregressive moving average model. This approximation is based on the limiting Wiener process. An example is also given.

Richard A. Johnson | M. Bagshaw

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