• The time-series data may contain missing values that need to be imputed. The time-series missing value imputation imputes these missing data points.
• It is performed only on missing values. The other values present in the dataset remain unchanged.

To impute missing values in the time-series data.

For analysis of time-series data without losing its variation.

When data do not contain any missing values.

The time interval for the data to be analyzed should be specified.

Time-series data with fixed time intervals or time-series data.

A complete time-series data for the specified time interval having no missing values.

• Mean
• Median
• Min
• Max
• Remove
• Constant
• Random
• Forward Fill
• Backward Fill
• Interpolate

• It does not account for the uncertainty of the imputations.
• It can introduce bias in the data.

Mean

It replaces the missing values with the mean of the non-missing values within each column separately and independently from the others.

• It only works on the column level.
• It can only be used with numerical data.

Median

It replaces the missing values with the median of the non-missing values within each column separately and independently from the others.

• It only works on the column level.
• It can only be used with numerical data.

Min

It replaces the missing values with the minimum value present in that column.

–

Max

It replaces the missing values with the maximum value present in that column.

–

Remove

It discards the rows that contain missing values.

• It can be used for a small amount of missing data (20-30%)
• Removing a large amount of data may cause considerable variations in the results.
• If there is a large amount of missing data, it is recommended to remove the complete column (If you want to remove a column, do not select that column while analyzing).

Constant

It replaces the missing values with the constant value that you have entered.

• It can only be used with numerical data.
• It can introduce bias in the data.

Random

It replaces the missing values with random values from that column.

• It can only be used with numerical data.

Forward Fill

It fills the missing value with the preceding value from the dataset.

For example, the number of people on Tuesday is missing in the time-series data. In this case, the Monday count becomes the Tuesday data.

Backward Fill

It fills the missing value with the succeeding value from the dataset.

For example, the number of people on Tuesday is missing in a time series data. In this case, the Wednesday count becomes the Tuesday data.

Interpolate

Using some pre-defined algorithms, it replaces the missing value by interpolating the existing values linearly in the dataset.

For example, the data points for twelve months are present in the time-series data, and the next value for the thirteenth month is missing. In this case, the twelve values are interpolated, and the value for the thirteenth month is calculated.