# standardize_vec: Standardize to Mean 0, Standard Deviation 1 (Center & Scale) In timetk: A Tool Kit for Working with Time Series

 standardize_vec R Documentation

## Standardize to Mean 0, Standard Deviation 1 (Center & Scale)

### Description

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.

### Usage

``````standardize_vec(x, mean = NULL, sd = NULL, silent = FALSE)

standardize_inv_vec(x, mean, sd)
``````

### Arguments

 `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 `mean` and `sd` parameters as a message.

### Details

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)

### Value

Returns a `numeric` vector with the standardization transformation applied.

• 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()`

### Examples

``````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))

``````

timetk documentation built on Sept. 22, 2023, 5:11 p.m.