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

standardize_vec {timetk} | 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)

### See Also

- 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))

[Package timetk version 2.6.1 Index]