How does the algorithm of the residualSimilarityModel looks like?

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Dear MATLAB Community,
I actually have a simple question and don't need a super scientific answer. What is the rough algorithm of the residualSimilarityModel?
I know that for the training every ensemble member gets a regression fit based on the equation defined in Method. And then I assume that in case of the command predictRUL() a additonal regression fit is performed for the input data and followed by a kNN classifiaction which checkes witch regression model of the training data is closest to the actual regression model and then predicting the RUL of the closest model.
However, in the description of "Method" is written: "Type of model trained using the fit function and used for residual generation, specified as one of the following:" What is meant by residual generation? This part is confusing me in my assumtion, for what is the residual of the regression model fit used? Somehow, I interpret that I am wrong with my kNN assumtion.
Does somone has a overview of how this model works?
Marvin Eckert
Marvin Eckert on 8 Sep 2020
Mhhh ... maybe I wine my question down. Can somone assume what is ment by this sentence?
> Then the test component's degradation data is used to compute 1-step prediction errors for each model (out of the trainings set).

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Accepted Answer

Ayush Gupta
Ayush Gupta on 11 Sep 2020
1-step prediction for a model is for a time series IT = {Y1, Y2 ,…, YT }.
At time T, we want to forecast YT+1, YT+2, YT+1, YT+2, …, YT+l
Where T is the forecast origin and l is forecast horizon.
1-step ahead forecast = Forecasted value YT+1
= E [YT+1 | YT, YT-1,…., Y1]
And 1-step prediction error is the margin by which it is varying with the actual value.

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