Example Concept: Interpolation is a method used to estimate missing values in a time series by using the known data points. Linear interpolation assumes a straight line between two known points, filling in missing values along this line. Alternatively, spline interpolation uses piecewise polynomials for a smoother curve. Forward filling (propagating the last known value) and backward filling (using the next known value) are simpler methods but may introduce bias if the missing data is extensive. Model-based methods, such as using ARIMA or Kalman filters, can also predict missing values by leveraging the time series' underlying patterns.