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I am referencing the example from this article on autoregressive machine learning using m4_monthly data.
Instead of using fixed loadings like loadings = c(4, 6), I would like to assign different weights for each time series across models. Below is a sample loadData data frame illustrating the intended structure:
Hi,
I am referencing the example from this article on autoregressive machine learning using m4_monthly data.
Instead of using fixed loadings like loadings = c(4, 6), I would like to assign different weights for each time series across models. Below is a sample loadData data frame illustrating the intended structure:
loadData <- expand.grid(
series = c("M1", "M2", "M750", "M1000"),
model = c("glmnet", "xgboost")
)
loadData$weight <- c(0.4, 0.5, 0.6, 0.9, 0.6, 0.5, 0.4, 0.1)
loadData
For example:
• series = M1, model = glmnet, weight = 0.4
• series = M1, model = xgboost, weight = 0.6
Could you advise how to implement this custom weighting setup within the ensemble_weighted function?
Thank you very much for your guidance.
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