Autocorrelation, also known as serial correlation, plays a crucial role in assessing forecasting models. It measures the degree of similarity between a given time series and a lagged version of itself over successive time intervals. In model assessment, autocorrelation helps identify patterns or trends not captured by the model.
If residuals from a forecast model show significant autocorrelation, it indicates that the model has failed to capture some explanatory information. This could be seasonality, cyclical patterns, or other forms of temporal dependence. Autocorrelation can lead to inefficient parameter estimates and unreliable statistical tests if not properly accounted for.
In contrast, lack of autocorrelation among residuals is desirable, indicating that the model has adequately captured all available information. Therefore, checking for autocorrelation is an essential step in validating the performance of forecasting models.
