Example Concept: Interpolation is a common method for handling missing data in time series. It involves estimating missing values based on the values of neighboring data points. Linear interpolation is often used, where the missing value is estimated as a straight line between the two nearest known values. Alternatively, more complex methods such as polynomial or spline interpolation can be applied for datasets with non-linear trends. These methods are particularly useful when the missing data points are not consecutive and the overall trend of the data is known.