D3A-TS: Denoising-Driven Data Augmentation in Time Series
David Solis-Martin Juan Galan-Paez Joaquin Borrego-Diaz
It has been demonstrated that the amount of data is crucial in data-driven
machine learning methods. Data is always valuable, but in some tasks, it is
almost like gold. This occurs in engineering areas where data is 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. 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 approach involves the use of diffusion probabilistic
models, 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.