This article presents a methodology for building measures of influence in regression models with time series data. We introduce statistics that measure the influence of each observation on the parameter estimates and on the forecasts. These statistics take into account the autocorrelation of the sample. The first statistic can be decomposed to measure the change in the univariate autoregressive integrated moving average parameters, the transfer-function parameters, and the interaction between both. For independent data, they reduced to theDstatistic considered by Cook in the standard regression model. These statistics can be easily computed using standard time series software. Their performance is analyzed in an example in which they are shown to be useful in identifying important events, such as additive outliers and trend shifts, in time series data.