Robust regression techniques, called S-estimators, are developed to handle contaminated data in time series analysis. These methods help accurately estimate autoregressive parameters and fit trends in time series data, even when some data points are unreliable. The S-estimators are designed to be resistant to errors caused by outliers or anomalies in the data, making them useful tools for robust analysis.