decompose_methods: Methods that power time_decompose()

In anomalize: Tidy Anomaly Detection

Methods that power time_decompose()

Description

Methods that power time_decompose()

Usage

decompose_twitter(
  data,
  target,
  frequency = "auto",
  trend = "auto",
  message = TRUE
)

decompose_stl(data, target, frequency = "auto", trend = "auto", message = TRUE)

Arguments

data	A tibble or tbl_time object.
target	A column to apply the function to
frequency	Controls the seasonal adjustment (removal of seasonality). Input can be either "auto", a time-based definition (e.g. "1 week"), or a numeric number of observations per frequency (e.g. 10). Refer to time_frequency().
trend	Controls the trend component For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder. For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.
message	A boolean. If TRUE, will output information related to tbl_time conversions, frequencies, and trend / median spans (if applicable).

Value

A tbl_time object containing the time series decomposition.

References

• The "twitter" method is used in Twitter's AnomalyDetection package

See Also

time_decompose()

Examples

library(dplyr)

tidyverse_cran_downloads %>%
    ungroup() %>%
    filter(package == "tidyquant") %>%
    decompose_stl(count)

anomalize documentation built on Nov. 2, 2023, 5:13 p.m.