Machine Learning Idea – Implicit Error Function

I’m currently doing some calculation on weather data. Just a time series.

I thought I get inspired by an equation.

dataIn – dataOut = dataModel

After some guessing I get the error equation. Here dataIn, dataout, … are >= 0. They are scaled from 0 to 1. I make some odd looking equation. sqrt(dataIn) – sqrt(dataOut) = sqrt(dataModel). Even though it does not follow above equation.

dataIn + dataOut – 2*((dataIn*dataOut)**.5) = dataModel(dataIn)

Here dataIn + dataOut – 2*((dataIn*dataOut)**0.5) is my target value for my model function for a given dataIn. This because I could get ‘nan’ otherwise where the model update can get negative for dataModel values.

So iterating through all my dataIn(i), dataOut(i) values I get the parameters for my model.

Then to predict a value for a given value x as dataIn. I input that x in my model(dataIn = x). Get an equation:

x + dataOut – 2*((x*dataOut)**.5) = model(x)

From this equation I solve the possible dataOut values as one of the predicted value.

 

interesting error plot

Some sort of implicit error function. It bounced ?

implicit error function loss

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