Example Concept: In time series analysis, missing data can be addressed using interpolation methods like linear interpolation, which estimates missing values based on surrounding data points. Alternatively, forward filling (propagating the last observed value forward) or backward filling (using the next observed value) can be applied when the time series is assumed to have a consistent trend. For more complex patterns, model-based imputation using methods such as ARIMA or Kalman filters can be employed to estimate missing values based on the underlying statistical properties of the time series.