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how to measure Model performance in a uni-variate time series data?

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bushra raza
bushra raza on 19 Mar 2020
Commented: bushra raza on 15 Apr 2020
Hi,
i have univariate time series data sets of water levels named as: observed data and simulated data .
i want to transform the simulated data in a way that it represents the variability and mean of the observation.
i.e. need to fix simulated data as may be like 95% variability and the mean of the obsrvation.
Or even better, how can i apply a transfer function that would transform the simulation into the observation?
i have joined them in a timetable. the data is attached as a mat file . first column is timestamp, second is observed data, and third column is simulated data.
please guide me how can i present the simulated /model performance?
what other measures should i consider for this ?
i have calculated RMSE,and nasch sutcliffe efficiency coefficient as well,(files of codes are attached). but its hard to interpret the results.
i need some guidance on this too.
looking forward for your guidance.

  2 Comments

Peter Perkins
Peter Perkins on 14 Apr 2020
This doesn't seem like a MATLAB question. If you have simulated data, they must have come from a simulation model. It aseems like your questions ought to be
  • Is the model producing data "like" the real data?
  • If not, how should the simulation model be changed?
Looking at the simulated data for the second question seems like you are too late.
I don't think anyone will be able to help with this question as posed, because only you know the details of your data and your model.
bushra raza
bushra raza on 15 Apr 2020
thank you for the reply.
i have raised similar question with more clarity i guess on
i am still eager to show 95% variability of simulated data versus observed data.
i dont know about simulation model, but i just have its values.
if simulated data represent the past years records say 1963-2016 well, then it means simulated data can be used for future projections. as simulated data is upto 2099.

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