<p>It has been demonstrated that the amount of data is crucial in data-driven machine learning methods. Data are always valuable, but in some tasks, it is almost like gold. This occurs in engineering areas where data are scarce or very expensive to obtain, such as predictive maintenance, where faults are rare. In this context, a mechanism to generate synthetic data can be very useful. While in fields such as computer vision or natural language processing, synthetic data generation has been extensively explored with promising results, in other domains such as time series it has received less attention. Previous works have proposed techniques like geometric transformations, interpolation, and generative models like GANs and VAEs for time series data augmentation. However, the use of denoising models, particularly diffusion probabilistic models (DPMs), remains largely unexplored in this context. This work specifically focuses on studying and analyzing the use of different techniques for data augmentation in time series for classification and regression problems. The proposed D3A-TS methodology involves the use of DPMs, which have recently achieved successful results in the field of image processing, for data augmentation in time series. Additionally, the use of meta-attributes to condition the data augmentation process is investigated. The results highlight the high utility of this methodology in creating synthetic data to train classification and regression models. To assess the results, six different datasets from diverse domains were employed, showcasing versatility in terms of input size and output types. Finally, an extensive ablation study is conducted to further support the obtained outcomes.</p>