Example Concept: Interpolation is a widely used method for handling missing data in time series. It involves estimating the missing values based on the known data points surrounding them. Linear interpolation, for example, assumes a straight line between two known points and fills in missing values along this line. Other methods, such as polynomial or spline interpolation, can be used for more complex patterns. These techniques help maintain the continuity of the dataset, which is essential for accurate time series analysis and forecasting.