First, we explore the core concepts of foundation models, such as pretraining, transfer learning and fine-tuning. Second, we take a look at the advantages and disadvantages of foundation models in time series forecasting. While they can speed up the modeling and inference process, they might also not be the best solution for a particular project, meaning that we must still have a certain expertise to use them correctly and compare them with other methods. Then, we explore some of the major contributions to the field, including TimeGPT, Chronos, Moirai and TimesFM. We quickly discover their architectures, their capabilities and their limitations. Finally, we see TimeGPT in action to demonstrate how a foundation model can be used and how it compares to traditional methods.