Example Concept: In time series analysis, missing data can be addressed through methods like forward fill, backward fill, linear interpolation, or more advanced techniques such as Kalman filtering or ARIMA-based imputation. Forward fill uses the last known value to fill missing entries, while backward fill uses the next known value. Linear interpolation estimates missing values by assuming a linear relationship between known data points. Advanced methods like Kalman filtering or ARIMA-based imputation consider the underlying statistical properties and temporal dependencies of the series to provide more accurate estimates.