# normalize_vec: Normalize to Range (0, 1) In timetk: A Tool Kit for Working with Time Series

 normalize_vec R Documentation

## Normalize to Range (0, 1)

### Description

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.

### Usage

``````normalize_vec(x, min = NULL, max = NULL, silent = FALSE)

normalize_inv_vec(x, min, max)
``````

### Arguments

 `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 `min` and `max` 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

A `numeric` vector with the 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)

d10_daily <- m4_daily %>% dplyr::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))

``````

timetk documentation built on Nov. 2, 2023, 6:18 p.m.