In this thesis, we explored the generative adversarial network called uTSGAN to generate patterns from multivariate CAN bus time series dataset. Since the given data is unlabelled, unprocessed and highly imbalanced containing large amount of missing values, we have to define and discard a few timestamps and limit the focus of the study to the reduced subset involving patterns of the 10-second window size, which are categorised and clustered into majority and minority classes. To generate such an imbalanced set, we have used image based time series GAN called uTSGAN which transforms a time sequence into a spectrogram image and back to a time sequence within the same GAN framework. For the comparison, we also prepared a resampled (balanced) dataset from the imbalanced set to use in alternative experiments. This comparison evaluates the results of the conventional resampling approach against the baseline as well as our novel implementations. We propose two new methods using "cluster density based" and "sample loss based" techniques. Throughout the experimentation, the "cluster density based" GANs consistently achieved better results on some common and uncommon evaluations for multivariate and highly imbalanced sample sets. Among some regular evaluation methods, classification metrics such as balanced accuracy and precision provide a better understanding of experimentation results. The TRTS balanced accuracy and precision from "cluster density based" GAN achieves over 82% and 90% with an improvement of 20-30% and 14-18% respectively from that of baseline; the TSTR balanced accuracy of "cluster density based" increased by 10.6% from that of baseline and it show slightly better precision with respect to that of the baseline when compared on generated results from univariate experiments. Secondly, the alternative "resampling based" implementations show similar values to that of the baseline in TRTS and TSTR classifications. Simultaneously, More distinguished results are seen using a quantitative metric called Earth Mover’s Distance(EMD). We have used this distance measure to calculate the overall mean EMD and clusterwise mean EMD between real samples and fake (i.e. generated) samples. During their evaluations, "cluster density based" experiments showed significantly better results for not only majority but also minority clusters as compared to the results of baseline and "resampling based" experiments. At the end, we have opened a discussion on how one can utilize our findings in MAR problem aswell as improve the results by taking some precautionary measures.