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Recompose bands separating anomalies from "normal" observations

Source: R/time_recompose.R
time_recompose.Rd

Recompose bands separating anomalies from "normal" observations

Usage

time_recompose(data)

Arguments

data

A tibble or tbl_time object that has been processed with time_decompose() and anomalize().

Value

Returns a tbl_time object.

Details

The time_recompose() function is used to generate bands around the "normal" levels of observed values. The function uses the remainder_l1 and remainder_l2 levels produced during the anomalize() step and the season and trend/median_spans values from the time_decompose() step to reconstruct bands around the normal values.

The following key names are required: observed:remainder from the time_decompose() step and remainder_l1 and remainder_l2 from the anomalize() step.

See also

Time Series Anomaly Detection Functions (anomaly detection workflow):

Examples


library(dplyr)

data(tidyverse_cran_downloads)

# Basic Usage
tidyverse_cran_downloads %>%
    time_decompose(count, method = "stl") %>%
    anomalize(remainder, method = "iqr") %>%
    time_recompose()
#> # A time tibble: 6,375 × 11
#> # Index:         date
#> # Groups:        package [15]
#>    package date       observed season trend remainder remainder_l1 remainder_l2
#>    <chr>   <date>        <dbl>  <dbl> <dbl>     <dbl>        <dbl>        <dbl>
#>  1 broom   2017-01-01     1053 -1007. 1708.    352.         -1725.        1704.
#>  2 broom   2017-01-02     1481   340. 1731.   -589.         -1725.        1704.
#>  3 broom   2017-01-03     1851   563. 1753.   -465.         -1725.        1704.
#>  4 broom   2017-01-04     1947   526. 1775.   -354.         -1725.        1704.
#>  5 broom   2017-01-05     1927   430. 1798.   -301.         -1725.        1704.
#>  6 broom   2017-01-06     1948   136. 1820.     -8.11       -1725.        1704.
#>  7 broom   2017-01-07     1542  -988. 1842.    688.         -1725.        1704.
#>  8 broom   2017-01-08     1479 -1007. 1864.    622.         -1725.        1704.
#>  9 broom   2017-01-09     2057   340. 1887.   -169.         -1725.        1704.
#> 10 broom   2017-01-10     2278   563. 1909.   -194.         -1725.        1704.
#> # ℹ 6,365 more rows
#> # ℹ 3 more variables: anomaly <chr>, recomposed_l1 <dbl>, recomposed_l2 <dbl>