ESN

I have attempted to model chaotic nonlinear timeseries such as the closing stock prices of S&P 500 companies, using echo state networks, a variant of reservoir computing. In a way, it shows how much more efficient ESNs are compared to lstms or regression when it comes to dynamic systems. I have compared the efficiency of three different approaches for the same -

Multiple linear regression

RNN using lstms

ESN based RNNs

The code for the ESN model has been obtained from clemens korndörfer's repo as it was not available on the keras library I have set the hyperparameters as per optimality and efficiency