Structural breaks refer to abrupt changes in a time series data set that can significantly impact the results of statistical models if not properly accounted for. Identifying and adjusting for these breaks is crucial for accurate forecasting and inference, as they may indicate shifts in underlying processes or external interventions affecting the data.