Which technique is used to evaluate the residuals of a time series model?Review LaterAutocorrelation analysisDifferencingMoving averageSeasonal adjustment

The technique used to evaluate the residuals of a time series model is Autocorrelation analysis.

Here's a step-by-step explanation:

1. Fit a time series model to the data. This could be any type of time series model that is appropriate for the data, such as ARIMA, SARIMA, etc.

2. After fitting the model, calculate the residuals. The residuals are the difference between the observed values and the predicted values from the model.

3. Perform autocorrelation analysis on the residuals. Autocorrelation is a statistical method used to measure the correlation between a variable and a lagged version of itself. In this case, we're interested in the correlation between the residuals at different points in time.

4. Plot the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the residuals. These plots can provide insights into any patterns or trends in the residuals that were not captured by the model.

5. If the residuals are not correlated (i.e., the ACF and PACF plots show no significant spikes), this indicates that the model has adequately captured the information in the data. If the residuals are correlated, this suggests that there is still information in the data that the model has not captured, and a different model may be needed.