Detecting Outliers in Time Series Data

Abstract

Abstract Occasional large errors in data can have drastic effects on estimates for such quantities as correlation coefficients, regression coefficients, and spectral density estimates. In this article we investigate the effect of outliers on time series data by considering the influence function for the autocorrelations p(k) of a stationary time series. This influence function matrix is applied to simulated data, to power plant data, and to inventory data on nuclear materials.