In the previous segment, you learnt different components of time series and also tried to explore the data of air passenger traffic problem. You understood that the data has missing values. In this segment, you will learn how to handle missing values in a time series data.

Let’s revisit the methods of handling missing values.

**Mean Imputation:**Imputing the missing values with the overall mean of the data.

**Last observation carried forward:**We impute the missing values with its previous value in the data.

**Linear interpolation:**You draw a straight line joining the next and previous points of the missing values in the data.

**Seasonal + Linear interpolation:**This method is best applicable for the data with trend and seasonality. Here, the missing value is imputed with the average of the corresponding data point in the previous seasonal period and the next seasonal period of the missing value.

You learnt how to handle missing values. Now, let’s understand how we can apply this understanding to treat the missing values in Air passenger traffic dataset.

Here, we used the linear interpolation method to treat the missing values in the dataset. Let’s now answer a few questions based on your learnings in this segment.