Example Concept: Interpolation is a common method for handling missing data in time series, where missing values are estimated based on surrounding data points. Linear interpolation assumes a straight line between known data points, while spline interpolation uses a piecewise polynomial function for smoother estimates. Forward and backward filling propagate the last known value forward or backward to fill gaps. Model-based approaches, such as using ARIMA, can predict missing values by leveraging the underlying patterns and correlations within the time series data.