4 Techniques to Handle Missing values in Time Series Data

The real-world data often contain missing values. All types of the dataset including time-series data have the problem with missing values. The cause of missing values can be data corruption or failure to record data at any given time. Time Series models work with the complete data and therefore they require to impute the missing values prior to the modeling or actual time series analysis. Dropping the missing value is however an inappropriate solution, as we may lose the correlation of adjacent observation.