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 more complex methods like spline or polynomial interpolation can capture non-linear trends. Forward or backward filling (also known as "carry forward" or "carry backward") involves filling missing values with the last or next observed value, respectively. Model-based imputation uses statistical models, such as ARIMA or Kalman filters, to predict missing values based on the observed data patterns.