Apr 17, 2013 · I think you have two different types of questions there. One thing is what you ask in the title: "What are good RMSE values?" and another thing is how to compare models with …

It doesn't make much difference in practice. But your suggested multiplicative adjustment is more consistent with the way the seasonal factors are used in ETS (*,*,M) models.

The raw signals you show above appear to be unfiltered and uncalibrated. Appropriate filtering and calibration, with some artifact rejection will in effect normalize the data. The standard …

Apr 25, 2014 · Normalizing dummy variables makes no sense. Usually, normalization is used when the variables are measured on different scales such that a proper comparison is not …

Does normalizing data (to have zero mean and unity standard deviation) prior to performing a repeated k-fold cross-validation have any negative conquences such as overfitting? Note: this …

If I have two positive real numbers that can take on any value between 0 and some finite real number, how do I normalized the difference between these two numbers to [0,1] interval where …

For example, say I have an input vector [0, 10, 0, 10, 20] representing 0 of item 1, 10 of item 2, etc. If I want to train on this data, is there some intuitive difference between the normalized v...

By normalizing all features to a 0-1 range, it prevents certain features from having strong importance than others. Conversely, if you some features to have stronger importance than …

Apr 24, 2021 · Normalizing Flows [1-4] are a family of methods for constructing flexible learnable probability distributions, often with neural networks, which allow us to surpass the limitations of …

Jul 16, 2021 · When moving to a categorical/discrete sample space we have probability mass functions, and the notion of the derivative is unclear. There are works to adapt the normalizing …