Standardize to Mean 0, Standard Deviation 1 (Center & Scale)
Standardization is commonly used to center and scale numeric features to prevent one from dominating in algorithms that require data to be on the same scale.
standardize_vec(x, mean = NULL, sd = NULL, silent = FALSE) standardize_inv_vec(x, mean, sd)
x |
A numeric vector. |
mean |
The mean used to invert the standardization |
sd |
The standard deviation used to invert the standardization process. |
silent |
Whether or not to report the automated |
Standardization vs Normalization
Standardization refers to a transformation that reduces the range to mean 0, standard deviation 1
Normalization refers to a transformation that reduces the min-max range: (0, 1)
Normalization/Standardization: standardize_vec(), normalize_vec()
Box Cox Transformation: box_cox_vec()
Lag Transformation: lag_vec()
Differencing Transformation: diff_vec()
Rolling Window Transformation: slidify_vec()
Loess Smoothing Transformation: smooth_vec()
Fourier Series: fourier_vec()
Missing Value Imputation for Time Series: ts_impute_vec(), ts_clean_vec()
library(dplyr)
library(timetk)
d10_daily <- m4_daily %>% filter(id == "D10")
# --- VECTOR ----
value_std <- standardize_vec(d10_daily$value)
value <- standardize_inv_vec(value_std,
mean = 2261.60682492582,
sd = 175.603721730477)
# --- MUTATE ----
m4_daily %>%
group_by(id) %>%
mutate(value_std = standardize_vec(value))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.