normalize_vec {timetk} | R Documentation |
Normalization 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.
normalize_vec(x, min = NULL, max = NULL, silent = FALSE) normalize_inv_vec(x, min, max)
x |
A numeric vector. |
min |
The population min value in the normalization process. |
max |
The population max value in the normalization 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_norm <- normalize_vec(d10_daily$value) value <- normalize_inv_vec(value_norm, min = 1781.6, max = 2649.3) # --- MUTATE ---- m4_daily %>% group_by(id) %>% mutate(value_norm = normalize_vec(value))