Reservoir Computing for Time Series Prediction - Streamlit app

A streamlit app to explore Reservoir Computing for time series predictions.

How to run app:

Option 1: Run app on streamlit cloud:

Link to app: https://duncdennis-echostatenetworkviewer-esn-app-main-upload-an-dsrup8.streamlitapp.com/

Note: The website might run into problems if many people are visiting the app at the same time, since the streamlit server resources are shared among all users.

Option 2: Run app locally in conda environment:

0. (Install Anaconda if not already installed.)
1. Clone this repository and enter the main directory.
2. Create the environment with: conda create --name rc_streamlit_app --file requirements.txt python=3.9
3. Activate newly created environment with: conda activate rc_streamlit_app
4. Run app with streamlit run esn_app.py
5. App will open in browser.

Screenshot of app:

Features:

Create raw data:

• Either upload your own, or simulate data from a selection of dynamical systems.
• If you choose to simulate data from a dynamical system:
  • Choose from 18 dynamical systems as for example: Lorenz63, Roessler, KuramotoSivashinsky.
  • Adjust the parameters of the dynamical system
  • View the system equations.
  • View and measure the raw data.

Preprocess the raw data:

• Perform time delay embedding.
• Shift and scale the raw data.
• Add noise.
• View and measure the preprocessed data.

Split the preprocessed data into train and predict sections:

• Choose which parts of the preprocessed data is used for training and for testing (i.e. prediction).
• View the data split.

Build the reservoir computing setup:

• Adjust all the Reservoir Computing hyperparameters like reservoir dimension, spectral radius and more.
• View some RC parameters, like the Network or Input Matrix

Train the reservoir:

• Perform the training.
• View the quality of the training fit.

Predict with the reservoir:

• Predict the prediction-section of the preprocessed data using the trained reservoir.
• View and measure the predicted vs. the real data.

Turn on advanced features:

• Turn on advanced features by checking a checkbox.
• Advanced features include:
  • More advanced reservoir computing options that allow for an additional processing layer between the reservoir states and the output fit.
  • Additional option to "look-under-hood" of the reservoir. View internal reservoir states.

Note:

Everything is still beta at the moment. Things that are not yet implemented:

• Proper typing for all python files.
• Documentation and tutorial on how to use.
• Proper requirements.
• Reformat and further clean up of code.

Related repository:

• https://github.com/GLSRC/rescomp