tk_seasonal_diagnostics: Group-wise Seasonality Data Preparation

In timetk: A Tool Kit for Working with Time Series

Group-wise Seasonality Data Preparation

Description

tk_seasonal_diagnostics() is the preprocessor for plot_seasonal_diagnostics(). It helps by automating feature collection for time series seasonality analysis.

Usage

tk_seasonal_diagnostics(.data, .date_var, .value, .feature_set = "auto")

Arguments

.data	A tibble or data.frame with a time-based column
.date_var	A column containing either date or date-time values
.value	A column containing numeric values
.feature_set	One or multiple selections to analyze for seasonality. Choices include: "auto" - Automatically selects features based on the time stamps and length of the series. "second" - Good for analyzing seasonality by second of each minute. "minute" - Good for analyzing seasonality by minute of the hour "hour" - Good for analyzing seasonality by hour of the day "wday.lbl" - Labeled weekdays. Good for analyzing seasonality by day of the week. "week" - Good for analyzing seasonality by week of the year. "month.lbl" - Labeled months. Good for analyzing seasonality by month of the year. "quarter" - Good for analyzing seasonality by quarter of the year "year" - Good for analyzing seasonality over multiple years.

Details

Automatic Feature Selection

Internal calculations are performed to detect a sub-range of features to include useing the following logic:

• The minimum feature is selected based on the median difference between consecutive timestamps

• The maximum feature is selected based on having 2 full periods.

Example: Hourly timestamp data that lasts more than 2 weeks will have the following features: "hour", "wday.lbl", and "week".

Scalable with Grouped Data Frames

This function respects grouped data.frame and tibbles that were made with dplyr::group_by().

For grouped data, the automatic feature selection returned is a collection of all features within the sub-groups. This means extra features are returned even though they may be meaningless for some of the groups.

Transformations

The .value parameter respects transformations (e.g. .value = log(sales)).

Value

A tibble or data.frame with seasonal features

Examples

library(dplyr)

# ---- GROUPED EXAMPLES ----

# Hourly Data
m4_hourly %>%
    group_by(id) %>%
    tk_seasonal_diagnostics(date, value)

# Monthly Data
m4_monthly %>%
    group_by(id) %>%
    tk_seasonal_diagnostics(date, value)

# ---- TRANSFORMATION ----

m4_weekly %>%
    group_by(id) %>%
    tk_seasonal_diagnostics(date, log(value))

# ---- CUSTOM FEATURE SELECTION ----

m4_hourly %>%
    group_by(id) %>%
    tk_seasonal_diagnostics(date, value, .feature_set = c("hour", "week"))

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