Wakeda University, Tokyo, International Congress on Industrial and Applied Mathematics 2023

Presentation given at Wakeda University, Tokyo as part of the mini-symposium on "Randomization for simplified machine learning: Random Features and Reservoir Computers" at the 10th International Congress on Industrial and Applied Mathematics 2023.

Reservoir computers have proven to be powerful embedding machines for dynamical systems. However, bridging the gap from their machine learning origins to time series analysis is still relatively new, with great potential for novel discoveries. In this talk, we will outline what reservoir time series analysis is and why one should care about it amidst the ecosystem of other embedding-based techniques. We will then present some use cases and applications to motivate future work.