Example Concept: Interpolation is a common technique for handling missing data in time series, where missing values are estimated based on the values of neighboring data points. Linear interpolation assumes a straight line between known data points, while more advanced methods like spline or polynomial interpolation can capture non-linear trends. Alternatively, forward or backward filling methods use the last or next known value to fill gaps, which is simple but may introduce bias if the data has strong trends or seasonality.