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I have been doing a COVID-19 related project. Here is the question:

  • N = vector of daily new infected cases
  • D = vector of daily deaths
  • E[D] = estimation of daily deaths

N is a n-dimensional vector, n is around 60. E[D] is another n-dimensional vector. Under certain assumptions, each entry of E[D] can be calculated as a linear combination of the entries of N.

We want to find the vector N such that the E[D] derived from N has least mean squared error when compared to actual D data. I think a gradient descent algorithm is needed here. However, I am not very familiar with gradient descent.

This seems to be a basic data science problem, but I am kind of lost right now. Does anyone has any idea about which algorithm should I dig into?

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If inputs are the same and you expect multiple outputs, I would recommend you to look at multioutput models which can be of two types :

  • native multioutput algorithms
  • multiple single output regressors wrapped together. Sklearn’s multioutput does that.
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