In recent years, there has been significant growth in the application of deep learning methods for classification, anomaly detection, and forecasting of time series. However, only some studies address problems involving sparse or intermittent demand time series, since the availability of sparse databases is scarce. This work compares the performance of three data augmentation approaches based on generative models and provides the code used to generate synthetic sparse and non-sparse time series. The experiments are carried out using a newly created sparse time series database, ASTELCO, which is generated from real e-commerce data (STELCO) supplied by a mobile Internet Service Provider. For the sake of reproducibility and as an additional contribution to the community, we make both the STELCO and ASTELCO datasets publicly available, and openly release the implemented code.