# Missing Values (Temporal Causal Modeling)

Missing values in the input data are replaced with an imputed value. The following
replacement methods are available:

- Linear interpolation
- Replaces missing values by using a linear interpolation. The last valid value before the missing value and the first valid value after the missing value are used for the interpolation. If the first or last observation in the series has a missing value, then the two nearest non-missing values at the beginning or end of the series are used.
- Series mean
- Replaces missing values with the mean for the entire series.
- Mean of nearby points
- Replaces missing values with the mean of valid surrounding values. The span of nearby points is the number of valid values before and after the missing value that are used to compute the mean.
- Median of nearby points
- Replaces missing values with the median of valid surrounding values. The span of nearby points is the number of valid values before and after the missing value that are used to compute the median.
- Linear trend
- This option uses all non-missing observations in the series to fit a simple linear regression model, which is then used to impute the missing values.

Other settings:

- Maximum percentage of missing values (%)
- Specifies the maximum percentage of missing values that are allowed for any series. Series with more missing values than the specified maximum are excluded from the analysis.
- User-missing values
- This option specifies whether user-missing values are treated as valid data and therefore included in the series. By default, user-missing values are excluded and are treated like system-missing values, which are then imputed.