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.