The autocorrelation function is a measure of the correlation between observations of a time series that are separated by k time units (y t and y t–k).

The graphs for the autocorrelation function (ACF) of the ARIMA residuals include lines that represent the significance limits. Values that extend beyond the significance limits are …

Use Autocorrelation to calculate and plot the correlation between observations of a time series. View the autocorrelation function plot to guide your choice of terms to include in an ARIMA …

The manager uses the autocorrelation function to determine which terms to include in an ARIMA model.

Collect enough data so that you can fully assess trends or patterns in the data. Minitab displays correlations for only the first n/4 lags. So if you have monthly data, you'll need a large sample …

Stat > Time Series > Autocorrelation. In Series, enter a column of numeric data that were collected at regular intervals and recorded in time order.

The partial autocorrelation function (PACF) is calculated from a recursive algorithm.

Guidelines for testing autocorrelation. A guideline based on large-sample normal approximation is often used to decide whether a specific sample autocorrelation is within sampling error of zero. …

The Durbin-Watson statistic tests for the presence of autocorrelation in the errors of a regression model. Autocorrelation means that the errors of adjacent observations are correlated.

The partial autocorrelation function is a measure of the correlation between observations of a time series that are separated by k time units (y t and y t–k), after adjusting for the presence of all …