Time series as data in the machine learning research area has been dominated by prediction and forecasting techniques. Ever since the inception of generative models, the interest in generating time series has increased. Time series data appears in many different fields with financial and medical gathering much of the interest. This thesis is instead focusing on the automotive field with a heavy focus on wheel speed data. The issue with wheel speed data, or any other vehicle signal, is that they take a long time to gather since a person has to drive around in order to get the data.  This thesis investigates the possibility to generate vehicle signals with a large focus on wheel speed signals. To better understand the difference between different car models and which vehicle signals are most useful. The classification of vehicle time series was done with a stacked LSTM network. A thorough analysis of the network parameters was made and an accuracy of over 80\% was achieved when classifying 6 different vehicle models. For time series generation a GAN with LSTM networks was used, based on CRNNGAN. The generated samples were evaluated by people experienced with the data and by using both PCA and t-SNE. The result is bad and is too noisy. Only one of the vehicle signals could be generated in a satisfying manner and that signal was significantly less complex since it was a binary signal being either 0 or 1.