Parameter fitting using Machine Learning techniques on time series
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I have a time variying quantity X(t) that can behave according to two different behaviors, let's call them A and B. Behavior A and B are respectively characterized by parameters a and b.
Two thinks I would like to do:
- be able to classify my time series Xi(t), according to which behavior they have, A or B
- measure the numerical value of the parameter corresponding to this behavior (I have no analytical formula so I was thinking doing this by ML as well).
I am new to Machine Learning. My questions:
- For the classification I was thinking of using LSTM networks. Is it the best option for my need? my time series usually have 1e3-1e4 elements. (what about if I have even longer datasets, say 1e05 elements?)
- Regarding the computation of the parameter values, should I need to train the neural networks with all the values I accept to sample (which will be the only candidate values I will be considering), or is there a Deep Learning method (or else) that I should use?
Thank you for your help and advice.