2021-07-19 Iker Esnaola-Gonzalez <mailto:iker.esnaola@tekniker.es> The DQTS (Data Quality for Time Series) ontology describes data quality dimensions for time series. It is aimed to be used as an extension of W3C's DQV (Data Quality Vocabulary) for representing dimensions. 2021-10-14 Data Quality for Time Series (DQTS) ontology dqts 0.1 Proportion of values available in a dataset. It is complementary to the proportion of missing values. completeness stable Proportion of completed observations available in a dataset. It is complementary to the proportion of missing observations. https://w3id.org/dqts# completeness of Observations stable Proportion of completed variables available in a dataset. It is complementary to proportion of missing variables. https://w3id.org/dqts# completeness of Variables stable Mean (of each variable) of the proportion of values within the confidence intervals of 80% in a dataset. https://w3id.org/dqts# consistency stable Proportion of variables with the correct format (e.g. numeric, String, ...) in a dataset. https://w3id.org/dqts# format stable Mean (of each variable) of the proportion of values within the confidence intervals of 99% in a dataset. https://w3id.org/dqts# moderation stable Proportion of variables with the expected name in a dataset. https://w3id.org/dqts# name stable Proportion of values within a boundaries established in a dataset. https://w3id.org/dqts# range stable Proportion of unique timestamps in a dataset. https://w3id.org/dqts# time uniqueness stable Proportion of waiting times between observations considered correct (less than the maximum established) in a dataset. https://w3id.org/dqts# timeliness stable Mean (of each variable) of the proportion of values within the confidence intervals of 95% in a dataset. https://w3id.org/dqts# typicality stable