Parameter fitting using Machine Learning techniques on time series

2 views (last 30 days)
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:
  1. be able to classify my time series Xi(t), according to which behavior they have, A or B
  2. 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:
  1. 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?)
  2. 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.

Answers (0)


Find more on Deep Learning with Time Series and Sequence Data in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!