Missing data in Time Series

Missing data is a well-known problem in Data Science. Missing data can cause problems in data analysis and modeling. Therefore rows with missing values need to be deleted or the missing values should be filled with reasonable values. The process of filling the missing values is called Imputation. But when dealing with time series this process is referred to as Interpolation.

In this blog, I will talk about some ways to fill missing values in Time Series.

Mean Interpolation

Mean Interpolation is one of the simplest and easiest methods used to fill the missing values. In this method the missing values are filled with the mean.

Median Interpolation

In this method the missing values are filled with the median.

Mode Interpolation

In this method the missing values are filled with the mode.