Visualize Multiple Seasonality Features for One or More Time Series

An interactive and scalable function for visualizing time series seasonality. Plots are available in interactive plotly (default) and static ggplot2 format.

Usage

plot_seasonal_diagnostics(
  .data,
  .date_var,
  .value,
  .facet_vars = NULL,
  .feature_set = "auto",
  .geom = c("boxplot", "violin"),
  .geom_color = "#2c3e50",
  .geom_outlier_color = "#2c3e50",
  .title = "Seasonal Diagnostics",
  .x_lab = "",
  .y_lab = "",
  .interactive = TRUE
)

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

.facet_vars

One or more grouping columns that broken out into ggplot2 facets. These can be selected using tidyselect() helpers (e.g contains()).

.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.

.geom

Either "boxplot" or "violin"

.geom_color

Geometry color. Line color. Use keyword: "scale_color" to change the color by the facet.

.geom_outlier_color

Color used to highlight outliers.

.title

Plot title.

.x_lab

Plot x-axis label

.y_lab

Plot y-axis label

.interactive

If TRUE, returns a plotly interactive plot. If FALSE, returns a static ggplot2 plot.

Value

A plotly or ggplot2 visualization

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)).

Examples

# \donttest{
library(dplyr)
library(timetk)

# ---- MULTIPLE FREQUENCY ----
# Taylor 30-minute dataset from forecast package
taylor_30_min
#> # A tibble: 4,032 × 2
#>    date                value
#>    <dttm>              <dbl>
#>  1 2000-06-05 00:00:00 22262
#>  2 2000-06-05 00:30:00 21756
#>  3 2000-06-05 01:00:00 22247
#>  4 2000-06-05 01:30:00 22759
#>  5 2000-06-05 02:00:00 22549
#>  6 2000-06-05 02:30:00 22313
#>  7 2000-06-05 03:00:00 22128
#>  8 2000-06-05 03:30:00 21860
#>  9 2000-06-05 04:00:00 21751
#> 10 2000-06-05 04:30:00 21336
#> # ℹ 4,022 more rows

# Visualize series
taylor_30_min %>%
    plot_time_series(date, value, .interactive = FALSE)


# Visualize seasonality
taylor_30_min %>%
    plot_seasonal_diagnostics(date, value, .interactive = FALSE)


# ---- GROUPED EXAMPLES ----
# m4 hourly dataset
m4_hourly
#> # A tibble: 3,060 × 3
#>    id    date                value
#>    <fct> <dttm>              <dbl>
#>  1 H10   2015-07-01 12:00:00   513
#>  2 H10   2015-07-01 13:00:00   512
#>  3 H10   2015-07-01 14:00:00   506
#>  4 H10   2015-07-01 15:00:00   500
#>  5 H10   2015-07-01 16:00:00   490
#>  6 H10   2015-07-01 17:00:00   484
#>  7 H10   2015-07-01 18:00:00   467
#>  8 H10   2015-07-01 19:00:00   446
#>  9 H10   2015-07-01 20:00:00   434
#> 10 H10   2015-07-01 21:00:00   422
#> # ℹ 3,050 more rows

# Visualize series
m4_hourly %>%
    group_by(id) %>%
    plot_time_series(date, value, .facet_scales = "free", .interactive = FALSE)


# Visualize seasonality
m4_hourly %>%
    group_by(id) %>%
    plot_seasonal_diagnostics(date, value, .interactive = FALSE)


# }