tk_augment_timeseries: Add many time series features to the data

In business-science/timetk: A Tool Kit for Working with Time Series

Add many time series features to the data

Description

Add many time series features to the data

Usage

tk_augment_timeseries_signature(.data, .date_var = NULL)

Arguments

.data	A time-based tibble or time-series object.
.date_var	For tibbles, a column containing either date or date-time values. If NULL, the time-based column will interpret from the object (tibble, xts, zoo, etc).

Details

tk_augment_timeseries_signature() adds 25+ time series features including:

• Trend in Seconds Granularity: index.num

• Yearly Seasonality: Year, Month, Quarter

• Weekly Seasonality: Week of Month, Day of Month, Day of Week, and more

• Daily Seasonality: Hour, Minute, Second

• Weekly Cyclic Patterns: 2 weeks, 3 weeks, 4 weeks

Value

Returns a tibble object describing the timeseries.

See Also

Augment Operations:

• tk_augment_timeseries_signature() - Group-wise augmentation of timestamp features

• tk_augment_holiday_signature() - Group-wise augmentation of holiday features

• tk_augment_slidify() - Group-wise augmentation of rolling functions

• tk_augment_lags() - Group-wise augmentation of lagged data

• tk_augment_differences() - Group-wise augmentation of differenced data

• tk_augment_fourier() - Group-wise augmentation of fourier series

Underlying Function:

• tk_get_timeseries_signature() - Returns timeseries features from an index

Examples

library(dplyr)

m4_daily %>%
    group_by(id) %>%
    tk_augment_timeseries_signature(date)

business-science/timetk documentation built on Feb. 1, 2024, 10:39 a.m.