Time Series: use of Box-Cox to reduce the "noise" I am researching the best method to use with time series. FBprophet (Python) seems like a strong option.
To prepare time series for Prophet I am thinking about using boxcox and inv_boxcox at the end
from fbprophet import Prophet
from scipy.stats import boxcox
from scipy.special import inv_boxcox

what is your opinion? Is boxcox helpful in reducing the noise of the Values?
Any tips and suggestions will be greatly appreciated
 A: Box-Cox transform is not a tool to reduce the noise. And I don't even know what you mean by reducing the noise.
Box-Cox transform is usually applied to make noise look like a symmetrical bell shaped distribution. Sometime people say to normalize, meaning making it normal distribution. It's similar in intent to applying log transform. It doesn't mean that Box-Cox transform will convert any noise into Gaussian or even bell shaped one. It does work sometimes though.
Then you need to define what you mean by reducing the noise. For instance, if you divide your signal by 10, you'll certainly reduce noise as in absolute value of it will be smaller. Of course, you'll also reduce the signal with this, so the signal to noise ratio will stay the same.
I'm afraid transformations such as Box-Cox or logarithm cannot reduce noise in any meaningful way. On the other hand, smoothing can reduce the high frequency stochastic noise. The choice of noise reduction tool is driven by the nature of the data and the process that generates it.
A: I'm not familiar with FBProphet, but Box-Cox is often used with ARIMA time-series models.
Box-Cox won't reduce the variance of your data but it can make it more normally distributed and reduce the heteroskedasticity in your model. (See here and here) If you will be modeling your time series data using ARIMA or a linear model this could be important.
