Example Concept: Interpolation is a widely used technique for handling missing data in time series. It involves estimating missing values based on the values of neighboring data points. Linear interpolation assumes a straight line between known data points, while spline interpolation uses piecewise polynomials for a smoother fit. These methods are effective when the missing data is relatively small and the time series is continuous. For more significant gaps or non-linear patterns, model-based imputation using algorithms like ARIMA or machine learning models can provide more accurate estimates by capturing underlying trends and seasonality.