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I am trying to understand how multivariate data preprocessing works but there are some questions in my mind.

For example, I can do data smoothing, transformation (box-cox, differentiation), noise removal in univariate data (for any machine learning problem. Not only time series forecasting). But what if one variable is not noisy and the other is noisy? Or one is not smooth and another one is smooth (i will need to sliding window avg. for one variable but not the other one.) What will be the case? What should I do?

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Well i have an idea about my own question for who is interested: I think transforming-smoothing variables which are not stationary-smooth-has normal distribution will be enough. If that wont help, try to transform-smooth all variables

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