Example Concept: In time series analysis, missing data can be addressed using methods such as forward fill, backward fill, linear interpolation, or model-based imputation. Forward fill uses the last known value to fill subsequent missing entries, while backward fill uses the next known value. Linear interpolation estimates missing values by assuming a linear trend between known data points. Model-based imputation involves using statistical models like ARIMA to predict missing values based on the observed data pattern. Each method should be chosen based on the data characteristics and the analysis objectives.