Comments (4)
Hello,
If the predictions of your model is not good, you can try to tune the hyper-parameters ( see https://reservoirpy.readthedocs.io/en/latest/user_guide/hyper.html ).
Otherwise, I would say that predicting stock market is a very difficult task, so I'm not surprised that you have worse results than on Mackey-Glass or Lorenz.
Is ReservoirPy designed to only predict on chaotic structured data?
Reservoir computing in general is a good framework for chaotic data, but it can be used for other tasks that are not really chaotic.
Any examples of Stock Market data forecasting?
As far as I know, not with ReservoirPy, but there are a few articles here and there about using Echo State Network for this task (but I haven't read them, so I can't point specific ones).
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There are papers doing stock market prediction. One was done by Herbert Jaeger himself with students for a competition:
Iles et al (2007) Stepping forward through echoes of the past: forecasting with echo state networks
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=368e4c95c69d3499ff1a1e46cf80b855c3b97fb5
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Related Issues (20)
- Potential Error in Documentation HOT 1
- Segfault in classification notebook HOT 5
- Save/Load to/from disk HOT 2
- No warning is triggered when non-existing variable name is used
- Autograd - Feature Request HOT 1
- Mmap error with local parallelization with optuna from the tutorial HOT 1
- datasets.narma doesn't return input series HOT 5
- ValueError: Missing input data for node Reservoir-0.
- Fitting a model on non-temporal data HOT 1
- Feature Importance HOT 4
- Small-world reservoir matrices
- Rank list of degree of influence of input variables HOT 1
- I trying to forecast using reservoirpy HOT 1
- how to save and load a prediction model HOT 2
- Is the long term forecasting example opertion explanation correct HOT 3
- Understand and optimize ESN hyperparameters errors HOT 3
- Creating a reservoir of custom nodes HOT 2
- LMS doesn't work for single node readout HOT 1
- ESN Parameter Effects HOT 7
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