Package {tscv}

Title:	Functions and Utilities for Tidy Time Series Forecasting and Time Series Cross-Validation
Version:	1.0.0
Description:	Provides functions and tools for tidy time series analysis and forecasting as well as time series cross-validation. This is mainly a set of wrapper and helper functions as well as some extensions for the packages 'tsibble', 'fable', and 'fabletools'.
License:	GPL-3
URL:	https://github.com/ahaeusser/tscv, https://ahaeusser.github.io/tscv/
BugReports:	https://github.com/ahaeusser/tscv/issues
Depends:	R (≥ 4.1.0)
Imports:	rlang, dplyr, ggplot2, grDevices, tidyr, tidyselect, tibble, slider, lubridate, purrr, tsibble, forecast, fabletools, stats, qqplotr, scales, distributional, tidytext
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
Suggests:	knitr, rmarkdown, tidyverse, fable, feasts, testthat (≥ 3.0.0), covr
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-05-08 10:52:46 UTC; Alexander Häußer
Author:	Alexander Häußer [aut, cre, cph]
Maintainer:	Alexander Häußer <alexander-haeusser@gmx.de>
Repository:	CRAN
Date/Publication:	2026-05-13 07:30:02 UTC

Double Seasonal Holt-Winters model

Description

Specify a Double Seasonal Holt-Winters model for use with fabletools::model().

Usage

DSHW(formula, ...)

Arguments

Details

DSHW() is a model specification wrapper around forecast::dshw() for the fable, tsibble, and fabletools ecosystem.

The model is useful for time series with two important seasonal patterns, such as hourly data with daily and weekly seasonality.

The seasonal periods must be supplied through the periods argument.

Value

A model definition that can be used inside fabletools::model().

See Also

Other DSHW: fitted.DSHW(), forecast.DSHW(), model_sum.DSHW(), residuals.DSHW()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_load |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 28) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("DSHW" = DSHW(value, periods = c(24, 168)))

model_frame

Monthly time series data from the M4 Competition

Description

The data set contains 30 selected time series on a monthly basis from the M4 Competition.

Usage

data(M4_monthly_data)

Format

A time series object of class tibble with 7881 rows and 4 columns:

• index: Date and time index

• series: Time series ID from M4 forecasting competition

• category: Category from M4 forecasting competition

• value: Measurement variable

Source

M4 Competition

Examples

data(M4_monthly_data)

Quarterly time series data from the M4 Competition

Description

The data set contains 30 selected time series on a quarterly basis from the M4 Competition.

Usage

data(M4_quarterly_data)

Format

A time series object of class tibble with 2818 rows and 4 columns:

• index: Date and time index

• series: Time series ID from M4 forecasting competition

• category: Category from M4 forecasting competition

• value: Measurement variable

Source

M4 Competition

Examples

data(M4_quarterly_data)

Median model

Description

Specify a median benchmark model for use with fabletools::model().

Usage

MEDIAN(formula, ...)

Arguments

Details

MEDIAN() forecasts future values using the median of the observed response. The window() special controls whether the median is estimated using all observations, a fixed trailing window, or a rolling window.

Value

A model definition that can be used inside fabletools::model().

See Also

Other MEDIAN: fitted.MEDIAN(), forecast.MEDIAN(), model_sum.MEDIAN(), residuals.MEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("MEDIAN" = MEDIAN(value ~ window()))

model_frame

Seasonal mean model

Description

Specify a seasonal mean benchmark model for use with fabletools::model().

Usage

SMEAN(formula, ...)

Arguments

Details

SMEAN() forecasts each future observation using the historical mean of the matching seasonal position. Use the lag() special to define the seasonal period, for example lag("year") for monthly data.

Value

A model definition that can be used inside fabletools::model().

See Also

Other SMEAN: fitted.SMEAN(), forecast.SMEAN(), model_sum.SMEAN(), residuals.SMEAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("SMEAN" = SMEAN(value ~ lag("year")))

model_frame

Seasonal median model

Description

Specify a seasonal median benchmark model for use with fabletools::model().

Usage

SMEDIAN(formula, ...)

Arguments

Details

SMEDIAN() forecasts each future observation using the historical median of the matching seasonal position. Use the lag() special to define the seasonal period, for example lag("week") for hourly data with weekly seasonality.

Value

A model definition that can be used inside fabletools::model().

See Also

Other SMEDIAN: fitted.SMEDIAN(), forecast.SMEDIAN(), model_sum.SMEDIAN(), residuals.SMEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SMEDIAN" = SMEDIAN(value ~ lag("week")))

model_frame

Seasonal naive model with weekday-specific lags

Description

Specify a seasonal naive benchmark model for use with fabletools::model().

Usage

SNAIVE2(formula, ...)

Arguments

Details

SNAIVE2() is intended for hourly time series. It uses a daily lag for Tuesday to Friday observations and a weekly lag otherwise. This can be useful for electricity price or load data where weekdays have similar intraday structure and weekends require a weekly comparison.

Value

A model definition that can be used inside fabletools::model().

See Also

Other SNAIVE2: fitted.SNAIVE2(), forecast.SNAIVE2(), model_sum.SNAIVE2(), residuals.SNAIVE2()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SNAIVE2" = SNAIVE2(value))

model_frame

TBATS model

Description

Specify a TBATS model for use with fabletools::model().

Usage

TBATS(formula, ...)

Arguments

Details

TBATS() is a model specification wrapper around forecast::tbats() for the fable, tsibble, and fabletools ecosystem.

TBATS stands for trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components. It can be useful for time series with multiple or complex seasonal patterns.

The seasonal periods must be supplied through the periods argument.

Value

A model definition that can be used inside fabletools::model().

See Also

Other TBATS: fitted.TBATS(), forecast.TBATS(), model_sum.TBATS(), residuals.TBATS()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("TBATS" = TBATS(value, periods = c(24, 168)))

model_frame

Estimate autocorrelations of a numeric vector

Description

Estimate the sample autocorrelation function of a numeric vector.

Usage

acf_vec(x, lag_max = 24, ...)

Arguments

Details

acf_vec() is a small wrapper around stats::acf(). It returns the sample autocorrelations as a numeric vector and removes lag 0 from the output, because lag 0 is always equal to 1 and is usually not needed for diagnostics.

Value

A numeric vector containing the sample autocorrelations for lags 1 to lag_max.

See Also

Other data analysis: estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

library(dplyr)

x <- M4_monthly_data |>
  filter(series == first(series)) |>
  pull(value)

acf_vec(
  x = x,
  lag_max = 12
)

Check and prepare tsibble data

Description

Check that the input data is a valid regular and ordered tsibble, fill implicit gaps if requested, and convert wide data to long format.

Usage

check_data(data, fill_missing = TRUE)

Arguments

Details

check_data() is a data preparation helper for time series workflows. It performs three tasks:

• checks that data is a tsibble;

• checks that the time index is regular and ordered by key and index;

• optionally turns implicit missing values into explicit missing values using fill_gaps().

If the input data has no key variables, it is treated as wide data and is converted to long format. The resulting output contains the original index column, a variable column containing the former column names, and a value column containing the corresponding observations.

Existing explicit missing values are not changed.

Value

A tsibble prepared for downstream use. Wide data is returned in long format with one measurement variable named value.

See Also

Other data preparation: interpolate_missing(), smooth_outlier()

Examples

library(dplyr)
library(tsibble)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395")) |>
  as_tsibble(
    index = index,
    key = series
  )

check_data(data)

wide_data <- data |>
  as_tibble() |>
  select(index, series, value) |>
  tidyr::pivot_wider(
    names_from = series,
    values_from = value
  ) |>
  as_tsibble(index = index)

check_data(wide_data)

Hourly electricity load (actual values and forecasts)

Description

Hourly tibble with actual electricity loads and load forecasts from the ENTSO-E Transparency Platform. The data set contains time series data from 2019-01-01 00:00:00 to 2019-12-31 23:00:00 for 8 bidding zones within Europe (DE, DK1, ES, FI, FR, NL, NO1, SE1). The original data are on a quarter-hourly basis (15-minutes interval), but aggregated to hourly data.

Usage

data(elec_load)

Format

A time series object of class tibble with 140.160 rows and 5 columns:

• time: Date and time index

• item: Time series name

• unit: Measured unit

• bidding_zone: Bidding zone

• value: Measurement variable

Source

ENTSO-E Transparency Platform

Examples

data(elec_load)

Hourly day-ahead electricity spot prices

Description

Hourly tibble with day-ahead electricity spot prices from the ENTSO-E Transparency Platform. The data set contains time series data from 2019-01-01 00:00:00 to 2020-12-31 23:00:00 for 8 bidding zones within Europe (DE, DK1, ES, FI, FR, NL, NO1, SE1).

Usage

data(elec_price)

Format

A time series object of class tibble with 140.352 rows and 5 columns:

• time: Date and time index

• item: Time series name

• unit: Measured unit

• bidding_zone: Bidding zone

• value: Measurement variable

Source

ENTSO-E Transparency Platform

Examples

data(elec_price)

Estimate autocorrelations by time series

Description

Estimate the sample autocorrelation function for one or more time series in a tibble.

Usage

estimate_acf(.data, context, lag_max = 24, level = 0.9, ...)

Arguments

Details

estimate_acf() groups the input data by the series identifier supplied in context and estimates the sample autocorrelation function for each time series separately.

The output contains one row per series and lag. The column bound contains an approximate significance threshold based on the selected confidence level. The logical column sign indicates whether the absolute autocorrelation is larger than this threshold.

Value

A tibble with the series identifier and the columns type, lag, value, bound, and sign.

See Also

Other data analysis: acf_vec(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

estimate_acf(
  .data = data,
  context = context,
  lag_max = 12
)

Estimate kurtosis

Description

Estimate the kurtosis of a numeric distribution.

Usage

estimate_kurtosis(x, na_rm = TRUE)

Arguments

Details

The function computes the moment-based kurtosis

\frac{n \sum_i (x_i - \bar{x})^4} {\left(\sum_i (x_i - \bar{x})^2\right)^2}

Missing values are removed by default.

This returns the usual kurtosis, not excess kurtosis. A normal distribution has kurtosis close to 3.

Value

A numeric value giving the estimated kurtosis.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

x <- c(1, 2, 3, 4, 5, NA)

estimate_kurtosis(x)
estimate_kurtosis(x, na_rm = TRUE)

set.seed(123)
y <- rnorm(100)
estimate_kurtosis(y)

Estimate the mode of a distribution

Description

Estimate the mode of a numeric distribution using kernel density estimation.

Usage

estimate_mode(x, na_rm = TRUE, ...)

Arguments

Details

The function computes a kernel density estimate with stats::density() and returns the value of x at which the estimated density is largest.

Missing values are removed by default. Additional arguments are passed to stats::density(), for example bw, kernel, or n.

Value

A numeric value giving the estimated mode of the distribution.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

x <- c(1, 1, 2, 2, 2, 3, 4, NA)

estimate_mode(x)
estimate_mode(x, na_rm = TRUE)
estimate_mode(x, bw = "nrd0")

set.seed(123)
y <- rnorm(100, mean = 5)
estimate_mode(y)

Estimate partial autocorrelations by time series

Description

Estimate the sample partial autocorrelation function for one or more time series in a tibble.

Usage

estimate_pacf(.data, context, lag_max = 24, level = 0.9, ...)

Arguments

Details

estimate_pacf() groups the input data by the series identifier supplied in context and estimates the sample partial autocorrelation function for each time series separately.

The output contains one row per series and lag. The column bound contains an approximate significance threshold based on the selected confidence level. The logical column sign indicates whether the absolute partial autocorrelation is larger than this threshold.

Value

A tibble with the series identifier and the columns type, lag, value, bound, and sign.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

estimate_pacf(
  .data = data,
  context = context,
  lag_max = 12
)

Estimate skewness

Description

Estimate the skewness of a numeric distribution.

Usage

estimate_skewness(x, na_rm = TRUE)

Arguments

Details

The function computes the moment-based skewness

\frac{\frac{1}{n}\sum_i (x_i - \bar{x})^3} {\left(\frac{1}{n}\sum_i (x_i - \bar{x})^2\right)^{3/2}}

Missing values are removed by default. Positive values indicate a distribution with a longer or heavier right tail; negative values indicate a distribution with a longer or heavier left tail.

Value

A numeric value giving the estimated skewness.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

x <- c(1, 2, 3, 4, 10, NA)

estimate_skewness(x)
estimate_skewness(x, na_rm = TRUE)

set.seed(123)
y <- rexp(100)
estimate_skewness(y)

Extract fitted values from a DSHW model

Description

Extract fitted values from a fitted DSHW model.

Usage

## S3 method for class 'DSHW'
fitted(object, ...)

Arguments

Value

Fitted values.

See Also

Other DSHW: DSHW(), forecast.DSHW(), model_sum.DSHW(), residuals.DSHW()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_load |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 28) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("DSHW" = DSHW(value, periods = c(24, 168)))

fitted(model_frame)

Extract fitted values from a median model

Description

Extract fitted values from a fitted MEDIAN model.

Usage

## S3 method for class 'MEDIAN'
fitted(object, ...)

Arguments

Value

Fitted values.

See Also

Other MEDIAN: MEDIAN(), forecast.MEDIAN(), model_sum.MEDIAN(), residuals.MEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("MEDIAN" = MEDIAN(value ~ window()))

fitted(model_frame)

Extract fitted values from a seasonal mean model

Description

Extract fitted values from a fitted SMEAN model.

Usage

## S3 method for class 'SMEAN'
fitted(object, ...)

Arguments

Value

Fitted values.

See Also

Other SMEAN: SMEAN(), forecast.SMEAN(), model_sum.SMEAN(), residuals.SMEAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("SMEAN" = SMEAN(value ~ lag("year")))

fitted(model_frame)

Extract fitted values from a seasonal median model

Description

Extract fitted values from a fitted SMEDIAN model.

Usage

## S3 method for class 'SMEDIAN'
fitted(object, ...)

Arguments

Value

Fitted values.

See Also

Other SMEDIAN: SMEDIAN(), forecast.SMEDIAN(), model_sum.SMEDIAN(), residuals.SMEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SMEDIAN" = SMEDIAN(value ~ lag("week")))

fitted(model_frame)

Extract fitted values from a SNAIVE2 model

Description

Extract fitted values from a fitted SNAIVE2 model.

Usage

## S3 method for class 'SNAIVE2'
fitted(object, ...)

Arguments

Value

Fitted values.

See Also

Other SNAIVE2: SNAIVE2(), forecast.SNAIVE2(), model_sum.SNAIVE2(), residuals.SNAIVE2()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SNAIVE2" = SNAIVE2(value))

fitted(model_frame)

Extract fitted values from a TBATS model

Description

Extract fitted values from a fitted TBATS model.

Usage

## S3 method for class 'TBATS'
fitted(object, ...)

Arguments

Details

This method is used by fitted() when extracting fitted values from a mable containing a TBATS model.

Value

Fitted values.

See Also

Other TBATS: TBATS(), forecast.TBATS(), model_sum.TBATS(), residuals.TBATS()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("TBATS" = TBATS(value, periods = c(24, 168)))

fitted(model_frame)

Forecast a DSHW model

Description

Forecast a fitted DSHW model.

Usage

## S3 method for class 'DSHW'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A vector of forecast distributions.

See Also

Other DSHW: DSHW(), fitted.DSHW(), model_sum.DSHW(), residuals.DSHW()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_load |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 28) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("DSHW" = DSHW(value, periods = c(24, 168)))

forecast(model_frame, h = 24)

Forecast a median model

Description

Forecast a fitted MEDIAN model.

Usage

## S3 method for class 'MEDIAN'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A vector of forecast distributions.

See Also

Other MEDIAN: MEDIAN(), fitted.MEDIAN(), model_sum.MEDIAN(), residuals.MEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("MEDIAN" = MEDIAN(value ~ window()))

forecast(model_frame, h = 12)

Forecast a seasonal mean model

Description

Forecast a fitted SMEAN model.

Usage

## S3 method for class 'SMEAN'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A vector of forecast distributions.

See Also

Other SMEAN: SMEAN(), fitted.SMEAN(), model_sum.SMEAN(), residuals.SMEAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("SMEAN" = SMEAN(value ~ lag("year")))

forecast(model_frame, h = 12)

Forecast a seasonal median model

Description

Forecast a fitted SMEDIAN model.

Usage

## S3 method for class 'SMEDIAN'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A vector of forecast distributions.

See Also

Other SMEDIAN: SMEDIAN(), fitted.SMEDIAN(), model_sum.SMEDIAN(), residuals.SMEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SMEDIAN" = SMEDIAN(value ~ lag("week")))

forecast(model_frame, h = 24)

Forecast a SNAIVE2 model

Description

Forecast a fitted SNAIVE2 model.

Usage

## S3 method for class 'SNAIVE2'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A vector of forecast distributions.

See Also

Other SNAIVE2: SNAIVE2(), fitted.SNAIVE2(), model_sum.SNAIVE2(), residuals.SNAIVE2()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SNAIVE2" = SNAIVE2(value))

forecast(model_frame, h = 24)

Forecast a TBATS model

Description

Forecast a fitted TBATS model.

Usage

## S3 method for class 'TBATS'
forecast(object, new_data, specials = NULL, ...)

Arguments

Details

This method is used by forecast() when forecasting a mable containing a TBATS model.

Value

A vector of forecast distributions.

See Also

Other TBATS: TBATS(), fitted.TBATS(), model_sum.TBATS(), residuals.TBATS()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("TBATS" = TBATS(value, periods = c(24, 168)))

forecast(model_frame, h = 24)

Interpolate missing values

Description

Interpolate missing values in a numeric time series.

Usage

interpolate_missing(x, periods, ...)

Arguments

Details

interpolate_missing() is a small wrapper around forecast::na.interp(). The input vector is first converted to an msts object using the seasonal periods supplied in periods.

For non-seasonal time series, missing values are replaced using linear interpolation. For seasonal time series, forecast::na.interp() uses an STL-based approach: the series is decomposed, the seasonally adjusted series is interpolated, and the seasonal component is added back.

The function returns a plain numeric vector with the same length as the input.

Value

A numeric vector with missing values interpolated.

See Also

Other data preparation: check_data(), smooth_outlier()

Examples

library(dplyr)

x <- M4_monthly_data |>
  filter(series == first(series)) |>
  pull(value)

x_missing <- x
x_missing[c(10, 20, 30)] <- NA

x_interpolated <- interpolate_missing(
  x = x_missing,
  periods = 12
)

anyNA(x_missing)
anyNA(x_interpolated)

hourly <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 14) |>
  pull(value)

hourly_missing <- hourly
hourly_missing[c(24, 48, 72)] <- NA

interpolate_missing(
  x = hourly_missing,
  periods = c(24, 168)
)

Calculate the mean absolute error

Description

Calculate the mean absolute error of a numeric vector.

Usage

mae_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

mae_vec() computes the average absolute forecast error abs(truth - estimate). The metric is reported in the same units as the original data.

Value

A numeric value.

See Also

Other accuracy functions: make_accuracy(), make_errors(), mape_vec(), me_vec(), mpe_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

truth <- c(10, 20, 30)
estimate <- c(8, 22, 25)

mae_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
mae_vec(truth_na, estimate_na)

Estimate point forecast accuracy

Description

Estimate accuracy metrics for point forecasts generated from rolling-origin time series cross-validation.

Usage

make_accuracy(
  future_frame,
  main_frame,
  context,
  dimension = "split",
  benchmark = NULL
)

Arguments

Details

make_accuracy() compares point forecasts in future_frame with the observed values in main_frame. The two data sets are joined using the series identifier and time index defined in context.

Accuracy can be summarized along different cross-validation dimensions:

• dimension = "split" summarizes accuracy separately for each test split.

• dimension = "horizon" summarizes accuracy separately for each forecast horizon.

The following point forecast accuracy metrics are returned:

• ME: mean error.

• MAE: mean absolute error.

• MSE: mean squared error.

• RMSE: root mean squared error.

• MAPE: mean absolute percentage error.

• sMAPE: symmetric mean absolute percentage error.

• MPE: mean percentage error.

If benchmark is supplied, the function also computes the relative mean absolute error rMAE. The rMAE is calculated as the model's MAE divided by the MAE of the benchmark model for the same series and selected dimension.

Value

A tibble containing the forecast accuracy metrics. The output contains the series identifier, model name, selected dimension, dimension value n, metric name, and metric value.

See Also

Other accuracy functions: mae_vec(), make_errors(), mape_vec(), me_vec(), mpe_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

library(dplyr)
library(tsibble)
library(fabletools)
library(fable)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  as_tsibble(
    index = index,
    key = c(series, split)
  )

model_frame <- train_frame |>
  model(
    "SNAIVE" = SNAIVE(value ~ lag("year"))
  )

fable_frame <- model_frame |>
  forecast(h = 18)

future_frame <- make_future(
  fable = fable_frame,
  context = context
)

accuracy_horizon <- make_accuracy(
  future_frame = future_frame,
  main_frame = main_frame,
  context = context,
  dimension = "horizon"
)

accuracy_horizon

accuracy_split <- make_accuracy(
  future_frame = future_frame,
  main_frame = main_frame,
  context = context,
  dimension = "split"
)

accuracy_split

Calculate forecast errors and percentage errors

Description

Calculate forecast errors and percentage forecast errors for point forecasts.

Usage

make_errors(future_frame, main_frame, context)

Arguments

Details

make_errors() compares point forecasts in future_frame with the observed values in main_frame. The two data sets are joined by the series identifier and time index specified in context.

The forecast error is calculated as error = actual - point. The percentage forecast error is calculated as pct_error = (actual - point / point) * 100.

Positive errors indicate that the forecast is below the observed value. Negative errors indicate that the forecast is above the observed value.

The returned data contains:

• series_id: Unique identifier for the time series as specified in context.

• model: Forecasting model name.

• split: Train-test split identifier.

• horizon: Forecast horizon.

• error: Forecast error.

• pct_error: Percentage forecast error.

Value

A tibble containing forecast errors and percentage forecast errors.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), mape_vec(), me_vec(), mpe_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

library(dplyr)
library(tsibble)
library(fabletools)
library(fable)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  as_tsibble(
    index = index,
    key = c(series, split)
  )

model_frame <- train_frame |>
  model(
    "SNAIVE" = SNAIVE(value ~ lag("year"))
  )

fable_frame <- model_frame |>
  forecast(h = 18)

future_frame <- make_future(
  fable = fable_frame,
  context = context
)

error_frame <- make_errors(
  future_frame = future_frame,
  main_frame = main_frame,
  context = context
)

error_frame

Convert forecasts to a future frame

Description

Convert forecasts from a fable object to a standardized forecast table.

Usage

make_future(fable, context)

Arguments

Details

make_future() converts the output of forecast() into a tibble with a consistent structure for downstream evaluation, plotting, and accuracy calculation.

The returned future_frame contains one row per forecasted observation, time series, split, and model. It includes the following columns:

• the time index column specified by context$index_id;

• the series identifier column specified by context$series_id;

• model: the forecasting model name;

• split: the train-test split identifier;

• horizon: the forecast horizon within each series, split, and model;

• point: the point forecast, taken from the .mean column of the fable.

This format is used by functions such as make_accuracy() and make_errors().

Value

A tibble containing forecasts in standardized future_frame format.

See Also

Other time series cross-validation: make_split(), make_tsibble(), slice_test(), slice_train(), split_index()

Examples

library(dplyr)
library(tsibble)
library(fable)
library(fabletools)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  as_tsibble(
    index = index,
    key = c(series, split)
  )

model_frame <- train_frame |>
  model(
    "SNAIVE" = SNAIVE(value ~ lag("year"))
  )

fable_frame <- model_frame |>
  forecast(h = 18)

future_frame <- make_future(
  fable = fable_frame,
  context = context
)

future_frame

Create train-test splits for time series cross-validation

Description

Create a split frame with train and test indices for one or more time series.

Usage

make_split(
  main_frame,
  context,
  type,
  value,
  n_ahead,
  n_skip = 0,
  n_lag = 0,
  mode = "slide",
  exceed = TRUE
)

Arguments

Details

make_split() creates rolling-origin train-test splits for time series cross-validation. The output is used by functions such as slice_train() and slice_test() to extract the corresponding training and testing samples from main_frame.

The function supports two training-window modes:

• mode = "slide" creates a fixed-window approach. The training window has constant length and moves forward over time.

• mode = "stretch" creates an expanding-window approach. The training window starts at the first observation and grows over time.

The initial training window is controlled by type and value:

• type = "first" uses the first value observations as the initial training window.

• type = "last" keeps the last value observations for testing and derives the initial training window from the remaining sample.

• type = "prob" uses floor(value * n_total) observations as the initial training window.

The argument n_skip controls how far the rolling origin moves between consecutive splits. For non-overlapping test windows, use n_skip = n_ahead - 1.

Value

A tibble containing the split plan. The output has one row per time series and split, with list-columns train and test containing integer row positions.

See Also

Other time series cross-validation: make_future(), make_tsibble(), slice_test(), slice_train(), split_index()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series == "M23100")

# Fixed-window split plan
fixed_split <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "slide",
  exceed = FALSE
)

fixed_split

# Expanding-window split plan
expanding_split <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

expanding_split

Convert data to a tsibble

Description

Convert a tibble containing time series data to a tsibble.

Usage

make_tsibble(main_frame, context)

Arguments

Details

make_tsibble() is a small helper for time series cross-validation workflows. It uses the time index and series identifier supplied in context to create a regular tsibble.

The input data must contain the columns specified by context$index_id and context$series_id. The column specified by context$index_id is used as the time index, and the column specified by context$series_id is used as the key.

Value

A tsibble with the same columns as main_frame, using the index and key defined in context.

See Also

Other time series cross-validation: make_future(), make_split(), slice_test(), slice_train(), split_index()

Examples

library(dplyr)
library(tsibble)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

tsibble_frame <- make_tsibble(
  main_frame = main_frame,
  context = context
)

tsibble_frame

Calculate the mean absolute percentage error

Description

Calculate the mean absolute percentage error of a numeric vector.

Usage

mape_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

mape_vec() computes the average absolute percentage forecast error: abs((truth - estimate) / truth) * 100.

This metric is undefined when truth is zero and may return Inf or NaN in such cases.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), me_vec(), mpe_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

truth <- c(10, 20, 40)
estimate <- c(8, 22, 30)

mape_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
mape_vec(truth_na, estimate_na)

Calculate the mean error

Description

Calculate the mean error of a numeric vector.

Usage

me_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

me_vec() computes the average signed forecast error truth - estimate. Positive values indicate that forecasts are, on average, below the observed values. Negative values indicate that forecasts are, on average, above the observed values.

The metric is reported in the same units as the original data.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), mape_vec(), mpe_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

truth <- c(10, 20, 30)
estimate <- c(8, 22, 25)

me_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
me_vec(truth_na, estimate_na)

Summarize a DSHW model

Description

Return a short model label for a fitted DSHW model.

Usage

## S3 method for class 'DSHW'
model_sum(x)

Arguments

Value

A character string.

See Also

Other DSHW: DSHW(), fitted.DSHW(), forecast.DSHW(), residuals.DSHW()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_load |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 28) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("DSHW" = DSHW(value, periods = c(24, 168)))

model_frame

Summarize a median model

Description

Return a short model label for a fitted MEDIAN model.

Usage

## S3 method for class 'MEDIAN'
model_sum(x)

Arguments

Value

A character string.

See Also

Other MEDIAN: MEDIAN(), fitted.MEDIAN(), forecast.MEDIAN(), residuals.MEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("MEDIAN" = MEDIAN(value ~ window()))

model_frame

Summarize a seasonal mean model

Description

Return a short model label for a fitted SMEAN model.

Usage

## S3 method for class 'SMEAN'
model_sum(x)

Arguments

Value

A character string.

See Also

Other SMEAN: SMEAN(), fitted.SMEAN(), forecast.SMEAN(), residuals.SMEAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("SMEAN" = SMEAN(value ~ lag("year")))

model_frame

Summarize a seasonal median model

Description

Return a short model label for a fitted SMEDIAN model.

Usage

## S3 method for class 'SMEDIAN'
model_sum(x)

Arguments

Value

A character string.

See Also

Other SMEDIAN: SMEDIAN(), fitted.SMEDIAN(), forecast.SMEDIAN(), residuals.SMEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SMEDIAN" = SMEDIAN(value ~ lag("week")))

model_frame

Summarize a SNAIVE2 model

Description

Return a short model label for a fitted SNAIVE2 model.

Usage

## S3 method for class 'SNAIVE2'
model_sum(x)

Arguments

Value

A character string.

See Also

Other SNAIVE2: SNAIVE2(), fitted.SNAIVE2(), forecast.SNAIVE2(), residuals.SNAIVE2()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SNAIVE2" = SNAIVE2(value))

model_frame

Summarize a TBATS model

Description

Return a short model label for a fitted TBATS model.

Usage

## S3 method for class 'TBATS'
model_sum(x)

Arguments

Value

A character string.

See Also

Other TBATS: TBATS(), fitted.TBATS(), forecast.TBATS(), residuals.TBATS()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("TBATS" = TBATS(value, periods = c(24, 168)))

model_frame

Calculate the mean percentage error

Description

Calculate the mean percentage error of a numeric vector.

Usage

mpe_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

mpe_vec() computes the average signed percentage forecast error: ((truth - estimate) / truth) * 100. Positive values indicate that forecasts are, on average, below the observed values in percentage terms. Negative values indicate that forecasts are, on average, above the observed values.

This metric is undefined when truth is zero and may return Inf, -Inf, or NaN in such cases.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), mape_vec(), me_vec(), mse_vec(), rmse_vec(), smape_vec()

Examples

truth <- c(10, 20, 40)
estimate <- c(8, 22, 30)

mpe_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
mpe_vec(truth_na, estimate_na)

Calculate the mean squared error

Description

Calculate the mean squared error of a numeric vector.

Usage

mse_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

mse_vec() computes the average squared forecast error (truth - estimate)^2. The metric is reported in squared units of the original data.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), mape_vec(), me_vec(), mpe_vec(), rmse_vec(), smape_vec()

Examples

truth <- c(10, 20, 30)
estimate <- c(8, 22, 25)

mse_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
mse_vec(truth_na, estimate_na)

Estimate partial autocorrelations of a numeric vector

Description

Estimate the sample partial autocorrelation function of a numeric vector.

Usage

pacf_vec(x, lag_max = 24, ...)

Arguments

Details

pacf_vec() is a small wrapper around stats::pacf(). It returns the sample partial autocorrelations as a numeric vector for lags 1 to lag_max.

Value

A numeric vector containing the sample partial autocorrelations for lags 1 to lag_max.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), summarise_data(), summarise_split(), summarise_stats()

Examples

library(dplyr)

x <- M4_monthly_data |>
  filter(series == first(series)) |>
  pull(value)

pacf_vec(
  x = x,
  lag_max = 12
)

Plot data as a bar chart

Description

Create a bar chart for grouped numeric values.

Usage

plot_bar(
  data,
  x,
  y,
  position = "dodge",
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  flip = FALSE,
  reorder = FALSE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  bar_size = 0.75,
  bar_color = "grey35",
  bar_alpha = 1,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_bar() is a convenience wrapper around ggplot2::geom_bar() with stat = "identity". It is intended for data that already contains summarized values, for example accuracy metrics, counts, or grouped summary statistics.

The arguments x, y, facet_var, and color are passed as unquoted column names.

If color is supplied, bar fill colors are mapped to that variable and bar_color is ignored. If color is not supplied, all bars are drawn using bar_color.

The argument position controls how bars are displayed when color is supplied. Common values are "dodge" and "stack".

If flip = TRUE, the x-axis and y-axis are swapped using ggplot2::coord_flip().

If reorder = TRUE, tidytext::scale_x_reordered() is added. This is useful when the x-axis has been reordered within facets with tidytext::reorder_within().

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

stats <- summarise_stats(
  .data = data,
  context = context
)

plot_bar(
  data = stats,
  x = series,
  y = mean,
  title = "Average Value by Series",
  xlab = "Series",
  ylab = "Mean"
)

acf_data <- estimate_acf(
  .data = data,
  context = context,
  lag_max = 12
)

plot_bar(
  data = acf_data,
  x = lag,
  y = value,
  facet_var = series,
  title = "Autocorrelation by Series",
  subtitle = "Sample autocorrelation up to lag 12",
  xlab = "Lag",
  ylab = "ACF"
)

Plot a kernel density estimate

Description

Create a density plot for one or more numeric variables using kernel density estimation.

Usage

plot_density(
  data,
  x,
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  fill = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  line_width = 0.1,
  line_type = "solid",
  line_color = "grey35",
  fill_color = "grey35",
  fill_alpha = 0.5,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_density() is a convenience wrapper around ggplot2::geom_density(). It is useful for comparing the distribution of values across one or more time series, models, groups, or residual sets.

The arguments x, facet_var, color, and fill are passed as unquoted column names.

If color is supplied, both line color and fill color are mapped to that variable. In this case, line_color and fill_color are ignored. If color is not supplied, all density curves use line_color and fill_color.

Missing values are removed before plotting.

Additional arguments can be passed to ggplot2::geom_density() through ..., for example adjust, bw, or kernel.

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_bar(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

plot_density(
  data = data,
  x = value,
  facet_var = series,
  title = "Distribution of M4 Monthly Values",
  subtitle = "Kernel density estimates by series",
  xlab = "Value",
  ylab = "Density"
)

plot_density(
  data = data,
  x = value,
  color = series,
  title = "Distribution of M4 Monthly Values",
  subtitle = "Kernel density estimates by series",
  xlab = "Value",
  ylab = "Density",
  adjust = 1.2
)

Plot data as a histogram

Description

Create a histogram for one or more numeric variables.

Usage

plot_histogram(
  data,
  x,
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  fill = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  line_color = "grey35",
  line_width = 0.5,
  fill_color = "grey35",
  fill_alpha = 1,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_histogram() is a convenience wrapper around ggplot2::geom_histogram(). It is useful for visualizing the distribution of values across one or more time series, models, groups, or residual sets.

The arguments x, facet_var, color, and fill are passed as unquoted column names.

If color is supplied, both bar outline color and fill color are mapped to that variable. In this case, line_color and fill_color are ignored. If color is not supplied, all histogram bars use line_color and fill_color.

Missing values are removed before plotting.

Additional arguments can be passed to ggplot2::geom_histogram() through ..., for example bins, binwidth, or boundary.

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_bar(), plot_density(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

plot_histogram(
  data = data,
  x = value,
  facet_var = series,
  title = "Distribution of M4 Monthly Values",
  subtitle = "Histograms by series",
  xlab = "Value",
  ylab = "Count",
  bins = 20
)

plot_histogram(
  data = data,
  x = value,
  color = series,
  title = "Distribution of M4 Monthly Values",
  subtitle = "Grouped histograms by series",
  xlab = "Value",
  ylab = "Count",
  bins = 20,
  position = "identity",
  fill_alpha = 0.4
)

Plot data as a line chart

Description

Create a line chart for one or more time series or grouped numeric variables.

Usage

plot_line(
  data,
  x,
  y,
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  line_size = 0.75,
  line_type = "solid",
  line_color = "grey35",
  line_alpha = 1,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_line() is a convenience wrapper around ggplot2::geom_line() for plotting data in long format. It supports optional grouping by color, optional faceting, and the default tscv theme.

The arguments x, y, facet_var, and color are passed as unquoted column names.

If color is supplied, line colors are mapped to that variable and line_color is ignored. If color is not supplied, all lines are drawn using line_color.

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

plot_line(
  data = data,
  x = index,
  y = value,
  facet_var = series,
  title = "M4 Monthly Time Series",
  subtitle = "Selected monthly series from the M4 forecasting competition",
  xlab = "Time",
  ylab = "Value",
  caption = "Data: M4 Forecasting Competition"
)

plot_line(
  data = data,
  x = index,
  y = value,
  color = series,
  title = "M4 Monthly Time Series",
  xlab = "Time",
  ylab = "Value",
  line_size = 1.5
)

Plot data as a scatterplot

Description

Create a scatterplot for two variables.

Usage

plot_point(
  data,
  x,
  y,
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  point_size = 1.5,
  point_type = 16,
  point_color = "grey35",
  point_alpha = 1,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_point() is a convenience wrapper around ggplot2::geom_point(). It is useful for plotting relationships between two variables, for example observed values over time, forecast errors by horizon, or one numeric diagnostic against another.

The arguments x, y, facet_var, and color are passed as unquoted column names.

If color is supplied, point colors are mapped to that variable and point_color is ignored. If color is not supplied, all points are drawn using point_color.

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series == "M23100")

plot_point(
  data = data,
  x = index,
  y = value,
  title = "M4 Monthly Time Series",
  subtitle = "Series M23100",
  xlab = "Time",
  ylab = "Value"
)

acf_data <- estimate_acf(
  .data = M4_monthly_data |>
    filter(series %in% c("M23100", "M14395")),
  context = list(
    series_id = "series",
    value_id = "value",
    index_id = "index"
  ),
  lag_max = 12
)

plot_point(
  data = acf_data,
  x = lag,
  y = value,
  color = series,
  title = "Autocorrelation by Series",
  subtitle = "Sample autocorrelation up to lag 12",
  xlab = "Lag",
  ylab = "ACF",
  point_size = 4
)

Create a quantile-quantile plot

Description

Create a quantile-quantile plot for one or more numeric variables.

Usage

plot_qq(
  data,
  x,
  facet_var = NULL,
  facet_scale = "free",
  facet_nrow = NULL,
  facet_ncol = NULL,
  color = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  point_size = 2,
  point_shape = 16,
  point_color = "grey35",
  point_fill = "grey35",
  point_alpha = 0.25,
  line_width = 0.25,
  line_type = "solid",
  line_color = "grey35",
  line_alpha = 1,
  band_color = "grey35",
  band_alpha = 0.25,
  theme_set = theme_tscv(),
  theme_config = list(),
  ...
)

Arguments

Details

plot_qq() is a convenience wrapper around the qqplotr functions stat_qq_point(), stat_qq_line(), and stat_qq_band(). It is useful for checking whether values, residuals, or forecast errors approximately follow a theoretical distribution.

By default, the function creates a normal quantile-quantile plot with pointwise confidence bands.

The arguments x, facet_var, and color are passed as unquoted column names.

If color is supplied, point colors, line colors, and confidence-band fills are mapped to that variable. In this case, point_color, point_fill, line_color, and band_color are ignored. If color is not supplied, the fixed styling arguments are used.

Additional arguments can be passed to the underlying qqplotr statistics through ..., for example distributional arguments supported by qqplotr.

Additional theme settings can be supplied through theme_config. This should be a named list of arguments passed to ggplot2::theme().

Value

An object of class ggplot.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

plot_qq(
  data = data,
  x = value,
  facet_var = series,
  title = "QQ Plot of M4 Monthly Values",
  subtitle = "Normal quantile-quantile plots by series",
  xlab = "Theoretical quantiles",
  ylab = "Sample quantiles"
)

stats <- data |>
  group_by(series) |>
  mutate(value_centered = value - mean(value, na.rm = TRUE)) |>
  ungroup()

plot_qq(
  data = stats,
  x = value_centered,
  color = series,
  title = "QQ Plot of Centered M4 Monthly Values",
  subtitle = "Normal quantile-quantile plots by series",
  xlab = "Theoretical quantiles",
  ylab = "Sample quantiles"
)

Extract residuals from a DSHW model

Description

Extract residuals from a fitted DSHW model.

Usage

## S3 method for class 'DSHW'
residuals(object, ...)

Arguments

Value

Residuals.

See Also

Other DSHW: DSHW(), fitted.DSHW(), forecast.DSHW(), model_sum.DSHW()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_load |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 28) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("DSHW" = DSHW(value, periods = c(24, 168)))

residuals(model_frame)

Extract residuals from a median model

Description

Extract residuals from a fitted MEDIAN model.

Usage

## S3 method for class 'MEDIAN'
residuals(object, ...)

Arguments

Value

Residuals.

See Also

Other MEDIAN: MEDIAN(), fitted.MEDIAN(), forecast.MEDIAN(), model_sum.MEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("MEDIAN" = MEDIAN(value ~ window()))

residuals(model_frame)

Extract residuals from a seasonal mean model

Description

Extract residuals from a fitted SMEAN model.

Usage

## S3 method for class 'SMEAN'
residuals(object, ...)

Arguments

Value

Residuals.

See Also

Other SMEAN: SMEAN(), fitted.SMEAN(), forecast.SMEAN(), model_sum.SMEAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- M4_monthly_data |>
  filter(series == first(series)) |>
  as_tsibble(index = index)

model_frame <- train_frame |>
  model("SMEAN" = SMEAN(value ~ lag("year")))

residuals(model_frame)

Extract residuals from a seasonal median model

Description

Extract residuals from a fitted SMEDIAN model.

Usage

## S3 method for class 'SMEDIAN'
residuals(object, ...)

Arguments

Value

Residuals.

See Also

Other SMEDIAN: SMEDIAN(), fitted.SMEDIAN(), forecast.SMEDIAN(), model_sum.SMEDIAN()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SMEDIAN" = SMEDIAN(value ~ lag("week")))

residuals(model_frame)

Extract residuals from a SNAIVE2 model

Description

Extract residuals from a fitted SNAIVE2 model.

Usage

## S3 method for class 'SNAIVE2'
residuals(object, ...)

Arguments

Value

Residuals.

See Also

Other SNAIVE2: SNAIVE2(), fitted.SNAIVE2(), forecast.SNAIVE2(), model_sum.SNAIVE2()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("SNAIVE2" = SNAIVE2(value))

residuals(model_frame)

Extract residuals from a TBATS model

Description

Extract residuals from a fitted TBATS model.

Usage

## S3 method for class 'TBATS'
residuals(object, ...)

Arguments

Details

This method is used by residuals() when extracting residuals from a mable containing a TBATS model.

Value

Residuals.

See Also

Other TBATS: TBATS(), fitted.TBATS(), forecast.TBATS(), model_sum.TBATS()

Examples

library(dplyr)
library(tsibble)
library(fabletools)

train_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 21) |>
  as_tsibble(index = time)

model_frame <- train_frame |>
  model("TBATS" = TBATS(value, periods = c(24, 168)))

residuals(model_frame)

Calculate the root mean squared error

Description

Calculate the root mean squared error of a numeric vector.

Usage

rmse_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

rmse_vec() computes the square root of the mean squared forecast error. The metric is reported in the same units as the original data.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), mape_vec(), me_vec(), mpe_vec(), mse_vec(), smape_vec()

Examples

truth <- c(10, 20, 30)
estimate <- c(8, 22, 25)

rmse_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
rmse_vec(truth_na, estimate_na)

Create a tscv color scale

Description

Create a ggplot2 color scale based on a predefined tscv palette.

Usage

scale_color_tscv(palette = "main", discrete = TRUE, reverse = FALSE, ...)

Arguments

Details

scale_color_tscv() creates either a discrete or continuous color scale for the color aesthetic.

For discrete variables, the function uses ggplot2::discrete_scale(). For continuous variables, it uses ggplot2::scale_color_gradientn().

Available palettes are "main", "cool", "hot", "mixed", and "grey".

Value

A ggplot2 scale object.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_fill_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

plot_line(
  data = data,
  x = index,
  y = value,
  color = series,
  title = "M4 Monthly Time Series",
  subtitle = "Selected monthly series",
  xlab = "Time",
  ylab = "Value"
) +
  scale_color_tscv(palette = "main")

Create a tscv fill scale

Description

Create a ggplot2 fill scale based on a predefined tscv palette.

Usage

scale_fill_tscv(palette = "main", discrete = TRUE, reverse = FALSE, ...)

Arguments

Details

scale_fill_tscv() creates either a discrete or continuous fill scale for the fill aesthetic.

For discrete variables, the function uses ggplot2::discrete_scale(). For continuous variables, it uses ggplot2::scale_fill_gradientn().

Available palettes are "main", "cool", "hot", "mixed", and "grey".

Value

A ggplot2 scale object.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), theme_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

stats <- summarise_stats(
  .data = data,
  context = context
)

plot_bar(
  data = stats,
  x = series,
  y = mean,
  color = series,
  title = "Average Value by Series",
  xlab = "Series",
  ylab = "Mean"
) +
  scale_fill_tscv(palette = "main")

Slice test data from a split frame

Description

Extract test observations from a complete time series data set according to a split plan created by make_split().

Usage

slice_test(main_frame, split_frame, context)

Arguments

Details

slice_test() uses the row positions stored in the test list-column of split_frame to extract the corresponding observations from main_frame. The function is designed for rolling-origin time series cross-validation workflows.

The returned data has the same columns as main_frame, plus a split column identifying the train-test split. If main_frame contains multiple time series, slicing is performed separately for each series using the series identifier supplied in context.

When make_split() was called with n_lag > 0, the test data may include lagged observations before the forecast horizon.

Value

A tibble containing the sliced test data. It contains the same columns as main_frame, plus a split column.

See Also

Other time series cross-validation: make_future(), make_split(), make_tsibble(), slice_train(), split_index()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series == "M23100")

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

test_frame <- slice_test(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
)

test_frame

Slice training data from a split frame

Description

Extract training observations from a complete time series data set according to a split plan created by make_split().

Usage

slice_train(main_frame, split_frame, context)

Arguments

Details

slice_train() uses the row positions stored in the train list-column of split_frame to extract the corresponding observations from main_frame. The function is designed for rolling-origin time series cross-validation workflows.

The returned data has the same columns as main_frame, plus a split column identifying the train-test split. If main_frame contains multiple time series, slicing is performed separately for each series using the series identifier supplied in context.

Value

A tibble containing the sliced training data. It contains the same columns as main_frame, plus a split column.

See Also

Other time series cross-validation: make_future(), make_split(), make_tsibble(), slice_test(), split_index()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

main_frame <- M4_monthly_data |>
  filter(series == "M23100")

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
)

train_frame

Calculate the symmetric mean absolute percentage error

Description

Calculate the symmetric mean absolute percentage error of a numeric vector.

Usage

smape_vec(truth, estimate, na_rm = TRUE)

Arguments

Details

smape_vec() computes the symmetric mean absolute percentage error: abs(estimate - truth) / ((abs(truth) + abs(estimate)) / 2) * 100.

This metric is undefined when both truth and estimate are zero and may return NaN in such cases.

Value

A numeric value.

See Also

Other accuracy functions: mae_vec(), make_accuracy(), make_errors(), mape_vec(), me_vec(), mpe_vec(), mse_vec(), rmse_vec()

Examples

truth <- c(10, 20, 40)
estimate <- c(8, 22, 30)

smape_vec(truth, estimate)

truth_na <- c(10, 20, NA)
estimate_na <- c(8, 22, 25)
smape_vec(truth_na, estimate_na)

Identify and replace outliers

Description

Identify outliers in a numeric time series and replace them with smoothed values.

Usage

smooth_outlier(x, periods, ...)

Arguments

Details

smooth_outlier() is a small wrapper around forecast::tsoutliers(). The input vector is first converted to an msts object using the seasonal periods supplied in periods.

For non-seasonal time series, forecast::tsoutliers() uses a supsmu-based approach. For seasonal time series, the series is decomposed using STL and outliers are identified on the remainder component. Detected outliers are replaced by the replacement values returned by forecast::tsoutliers().

The function returns a plain numeric vector with the same length as the input.

Value

A numeric vector where detected outliers are replaced by smoothed values.

See Also

Other data preparation: check_data(), interpolate_missing()

Examples

library(dplyr)

x <- M4_monthly_data |>
  filter(series == first(series)) |>
  pull(value)

x_outlier <- x
x_outlier[20] <- x_outlier[20] * 5

x_smoothed <- smooth_outlier(
  x = x_outlier,
  periods = 12
)

x_outlier[20]
x_smoothed[20]

hourly <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 24 * 14) |>
  pull(value)

hourly_outlier <- hourly
hourly_outlier[48] <- hourly_outlier[48] * 5

smooth_outlier(
  x = hourly_outlier,
  periods = c(24, 168)
)

Create indices for train and test splits

Description

Create train and test indices for time series cross-validation.

Usage

split_index(
  n_total,
  n_init,
  n_ahead,
  n_skip = 0,
  n_lag = 0,
  mode = "slide",
  exceed = FALSE
)

Arguments

Details

split_index() creates integer index vectors for rolling-origin resampling. The function can create either fixed-window or expanding-window splits:

• mode = "slide" creates a fixed training window that moves forward over time.

• mode = "stretch" creates an expanding training window that always starts at the first observation.

The first training window contains n_init observations. Each test window contains n_ahead observations. The argument n_skip controls how many observations are skipped between consecutive split origins. For example, with n_ahead = 18 and n_skip = 17, consecutive test windows are non-overlapping.

If n_lag > 0, the test indices include lagged observations before the forecast horizon. This is useful when lagged predictors are needed for constructing features during testing.

If exceed = TRUE, additional out-of-sample test indices are allowed to exceed the original sample size.

Value

A list with two elements:

• train: a list of integer vectors with training indices.

• test: a list of integer vectors with test indices.

See Also

Other time series cross-validation: make_future(), make_split(), make_tsibble(), slice_test(), slice_train()

Examples

# Fixed-window splits
fixed_index <- split_index(
  n_total = 180,
  n_init = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "slide",
  exceed = FALSE
)

fixed_index

# Expanding-window splits
expanding_index <- split_index(
  n_total = 180,
  n_init = 120,
  n_ahead = 18,
  n_skip = 17,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

expanding_index

Summarise time series data

Description

Calculate basic data-quality summary statistics for one or more time series.

Usage

summarise_data(.data, context)

Arguments

Details

summarise_data() groups the input data by the series identifier supplied in context and returns one row per time series.

The function reports:

• start: first time index;

• end: last time index;

• n_obs: number of observations;

• n_missing: number of missing values;

• pct_missing: percentage of missing values;

• n_zeros: number of zero values;

• pct_zeros: percentage of zero values.

Value

A tibble containing one row per time series and the calculated summary statistics.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_split(), summarise_stats()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

summarise_data(
  .data = data,
  context = context
)

Summarise train-test splits

Description

Summarise the time and row-index ranges of training and test samples.

Usage

summarise_split(data)

Arguments

Details

summarise_split() is intended for data sets that contain sliced training and test observations from a time series cross-validation workflow. The input must be a tsibble with the columns split, sample, and id:

• split: train-test split identifier;

• sample: sample label, usually "train" or "test";

• id: integer row index within the original time series.

The function returns one row per split. For each split, it reports the time range and index range of each sample.

Value

A tibble containing the summarized split ranges.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_stats()

Examples

library(dplyr)
library(tsibble)

context <- list(
  series_id = "bidding_zone",
  value_id = "value",
  index_id = "time"
)

main_frame <- elec_price |>
  filter(bidding_zone == "DE") |>
  slice_head(n = 120)

split_frame <- make_split(
  main_frame = main_frame,
  context = context,
  type = "first",
  value = 48,
  n_ahead = 24,
  n_skip = 23,
  n_lag = 0,
  mode = "stretch",
  exceed = FALSE
)

train_frame <- slice_train(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  mutate(sample = "train")

test_frame <- slice_test(
  main_frame = main_frame,
  split_frame = split_frame,
  context = context
) |>
  mutate(sample = "test")

split_data <- bind_rows(train_frame, test_frame) |>
  group_by(bidding_zone, split, sample) |>
  mutate(id = row_number()) |>
  ungroup() |>
  as_tsibble(
    index = time,
    key = c(bidding_zone, split, sample)
  )

summarise_split(split_data)

Summarise distributional statistics by time series

Description

Calculate descriptive statistics for one or more time series.

Usage

summarise_stats(.data, context)

Arguments

Details

summarise_stats() groups the input data by the series identifier supplied in context and returns one row per time series.

The function reports:

• mean: arithmetic mean;

• median: median;

• mode: kernel-density based mode estimate;

• sd: standard deviation;

• p0: minimum;

• p25: 25 percent quantile;

• p75: 75 percent quantile;

• p100: maximum;

• skewness: moment-based skewness;

• kurtosis: moment-based kurtosis.

Missing values are removed when calculating the statistics.

Value

A tibble containing one row per time series and the calculated descriptive statistics.

See Also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

summarise_stats(
  .data = data,
  context = context
)

Custom ggplot2 theme for tscv

Description

Create the default ggplot2 theme used by the tscv package.

Usage

theme_tscv(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

Details

theme_tscv() returns a complete ggplot2 theme with a clean layout, subtle grid lines, bottom legend placement, and formatting for plot titles, subtitles, captions, facets, and axes.

The theme is used as the default theme in the plotting helpers of the tscv package, such as plot_line(), plot_point(), plot_bar(), plot_histogram(), plot_density(), and plot_qq().

Since the returned object is a regular ggplot2 theme, it can also be added directly to any ggplot object with + theme_tscv().

Value

A complete ggplot2 theme.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), tscv_cols(), tscv_pal()

Examples

library(dplyr)
library(ggplot2)

data <- M4_monthly_data |>
  filter(series == "M23100") |>
  mutate(index = as.Date(index))

# Plot with the default tscv theme
plot_line(
  data = data,
  x = index,
  y = value,
  title = "M4 Monthly Time Series",
  subtitle = "Series M23100",
  xlab = "Time",
  ylab = "Value",
  theme_set = theme_tscv()
)

# The same plot with the default ggplot2 grey theme
plot_line(
  data = data,
  x = index,
  y = value,
  title = "M4 Monthly Time Series",
  subtitle = "Series M23100",
  xlab = "Time",
  ylab = "Value",
  theme_set = theme_grey()
)

# theme_tscv() can also be added to regular ggplot objects
ggplot(data, aes(x = index, y = value)) +
  geom_line(color = "grey35") +
  labs(
    title = "M4 Monthly Time Series",
    subtitle = "Series M23100",
    x = "Time",
    y = "Value"
  ) +
  theme_tscv()

Extract tscv colors

Description

Extract named colors from the tscv color palette as hexadecimal color codes.

Usage

tscv_cols(...)

Arguments

Details

tscv_cols() returns the hexadecimal color codes used by the tscv package. If no color names are supplied, all available colors are returned.

Available colors are: "red", "green", "blue", "orange", "yellow", "light grey", and "dark grey".

Value

A named character vector of hexadecimal color codes.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_pal()

Examples

# Return all available tscv colors
tscv_cols()

# Return selected colors
tscv_cols("steelblue", "orange", "green")

# Use a tscv color in a plot
library(dplyr)

data <- M4_monthly_data |>
  filter(series == "M23100")

plot_line(
  data = data,
  x = index,
  y = value,
  title = "M4 Monthly Time Series",
  subtitle = "Series M23100",
  xlab = "Time",
  ylab = "Value",
  line_color = tscv_cols("steelblue")
)

Create a tscv color palette

Description

Create a color interpolation function based on one of the predefined tscv palettes.

Usage

tscv_pal(palette = "main", reverse = FALSE, ...)

Arguments

Details

tscv_pal() returns a palette function created with grDevices::colorRampPalette(). The returned function can be used to generate any number of colors from the selected palette.

Available palettes are:

• "main": blue, green, yellow.

• "cool": blue, green.

• "hot": yellow, orange, red.

• "mixed": blue, green, yellow, orange, red.

• "grey": light grey, dark grey.

Value

A palette function that takes an integer and returns hexadecimal color codes.

See Also

Other data visualization: plot_bar(), plot_density(), plot_histogram(), plot_line(), plot_point(), plot_qq(), scale_color_tscv(), scale_fill_tscv(), theme_tscv(), tscv_cols()

Examples

# Create a palette function
pal <- tscv_pal("main")

# Generate five colors
pal(5)

# Reverse the palette
tscv_pal("hot", reverse = TRUE)(5)

# Use generated colors in base R
barplot(
  height = c(3, 5, 4),
  col = tscv_pal("main")(3)
)