Example Concept: In time series analysis, handling missing data often involves using interpolation techniques like linear interpolation, spline interpolation, or polynomial interpolation to estimate missing values. Alternatively, forward filling (propagating the last observed value) or backward filling (using the next observed value) can be used, especially when the missing data is minimal. For more complex scenarios, model-based imputation methods such as Kalman filtering or ARIMA models can be employed to estimate missing values based on the underlying data patterns and trends.