Representation learning, a set of techniques of automatically discovering the useful features for further processing, significantly influences the final results of the task. However, most of current representation learning methods cannot be applied on the time series data due to the temporal nature of the time series. In order to extract features from this type of date, we propose three representation learning methods. The first one is based on time delayed embeddings, Wasserstein distance, and multidimensional scaling. The second method is based on the variational autoencoder, which is a powerful deep learning model that learns the representation of the data and even generates data of a similar pattern. The third method learns the representation of the data and even generates data of a similar pattern. The third method learns the representation efficiently based on mutual information, which belongs to the field of semi supervised learning. In this thesis, we first explain these three methods mathematically and use examples to illustrate them. Then we implement these methods and test them using standard dataset. Finally, we train these models using the time series data from ThyssenKrupp and demonstrate that the representations given by these three methods share certain consistency. In addition, this master thesis is based on the project with ThyssenKrupp. The time series data is also generated by the rotation of the products produced by ThyssenKrupp.