What method is used to fill missing values between the item start and item end date of a data set?

The correct method for filling missing values between item start and item end dates is commonly referred to as "middle filling." This technique typically involves inferring or estimating values that fall between two known points, effectively creating a smoother transition across gaps in the data.

In scenarios where you have start and end dates, middle filling would aim to populate the values with those that logically fit the sequence of time-based data. For example, if you have a date range with missing entries, middle filling ensures that the gaps are filled using information from surrounding points, enabling a coherent timeline.

The other methods mentioned have different specific applications. Back filling and future filling are used to propagate known values backward or forward in time, respectively. Average filling generally means replacing missing values with the average of available data points, a method not specifically suited for time series data where the sequence is crucial. Thus, "middle filling" aligns best with the objective of accurately completing the dataset based on the temporal context.