Abstract: The disclosure herein describes using probabilistic cardinality generation to partition time series data into subsets without entries that have duplicate time index values. Time series data including a plurality of categories and a time index category is obtained. Cardinality estimate values of the categories are generated using a probabilistic cardinality estimator and a candidate category is selected based on the cardinality estimate value of the selected candidate category. A time series identifier is generated using the candidate category and, based on the cardinality estimate value of the time series identifier indicating that subsets of the time series data partitioned based on the time series identifier lack entries with duplicate time index values, the time series data is partitioned into a set of time series grain data sets. The time series grain data sets can be used to train models using machine learning techniques.