Within the field of machine learning, Echo State Networks (ESN) are a type of neural
networks, in which input signals are mapped into higher dimensional spaces, which are
connected to outputs. ESNs are used in particular for sequential data, with applications to
forecasting. The benefit of using an Echo State Network to predict data is that it is model-free,
meaning that there does not need to be any prior knowledge about the data. This is in contrast to
using a model-based approach, which requires understanding of the system. This paper will
examine the application of ESNs to chaotic systems.