TMiss

Tmiss is a term encountered in data science and statistics to describe missing data with a temporal component. There is no universally standardized definition, and the meaning of tmiss can vary by field or software package. In general, it relates to how missing values occur along a time dimension.

In time-series or panel data, tmiss is commonly used to denote the amount or pattern of missing observations by time. It may be recorded as a vector or series tmiss, where each element corresponds to a time point or window and indicates the number of missing entries or a boolean indicator of any missingness. This information can be used for imputation, data quality assessment, or modeling decisions that depend on missingness over time.

As a preprocessing feature, tmiss can capture the intensity of missing data across time, helping models learn whether missingness carries information about the target variable or arises from systematic timing effects. It may also guide the choice of imputation method, the handling of irregular time grids, or the assessment of data reliability.

Example: In a seven-day dataset with daily measurements from multiple sources, a tmiss vector might be [0, 1, 0, 2, 0, 0, 1], signaling one missing value on day 2, two on day 4, and one on day 7.

See also: Missing data, Imputation, Time series, Data quality.