Scaling autocorrelated features If I have a bunch of autocorrelated features (for example, temperature, rainfall) that I want to use to predict a dependent variable, how should I scale these autocorrelated features before passing them through a time-series model?
Generally, I have seen people using mean and standard deviation for scaling, but since autocorrelated data aren't independent observations by definition how's the use of standard deviation valid?

Do not worry about autocorrelation in feature scaling. Feature scaling is a numerical, not a statistical issue. Statistically, estimating the standard deviation from an autocorrelated time series requires special adjustments. Numerically, you just want the numbers to be on roughly the same scale for stability of algorithms.
