  Conditional generation of time-dependent data is a task that has much
interest, whether for data augmentation, scenario simulation, completing
missing data, or other purposes. Recent works proposed a Transformer-based Time
series generative adversarial network (TTS-GAN) to address the limitations of
recurrent neural networks. However, this model assumes a unimodal distribution
and tries to generate samples around the expectation of the real data
distribution. One of its limitations is that it may generate a random
multivariate time series; it may fail to generate samples in the presence of
multiple sub-components within an overall distribution. One could train models
to fit each sub-component separately to overcome this limitation. Our work
extends the TTS-GAN by conditioning its generated output on a particular
encoded context allowing the use of one model to fit a mixture distribution
with multiple sub-components. Technically, it is a conditional generative
adversarial network that models realistic multivariate time series under
different types of conditions, such as categorical variables or multivariate
time series. We evaluate our model on UniMiB Dataset, which contains
acceleration data following the XYZ axes of human activities collected using
Smartphones. We use qualitative evaluations and quantitative metrics such as
Principal Component Analysis (PCA), and we introduce a modified version of the
Frechet inception distance (FID) to measure the performance of our model and
the statistical similarities between the generated and the real data
distributions. We show that this transformer-based CGAN can generate realistic
high-dimensional and long data sequences under different kinds of conditions.
