timetk (version 2.2.1)

time_series_cv: Time Series Cross Validation

Description

Create rsample cross validation sets for time series. This function produces a sampling plan starting with the most recent time series observations, rolling backwards. The sampling procedure is similar to rsample::rolling_origin(), but places the focus of the cross validation on the most recent time series data.

Usage

time_series_cv(
  data,
  date_var = NULL,
  initial = 5,
  assess = 1,
  skip = 1,
  lag = 0,
  cumulative = FALSE,
  slice_limit = n(),
  ...
)

Arguments

data

A data frame.

date_var

A date or date-time variable.

initial

The number of samples used for analysis/modeling in the initial resample.

assess

The number of samples used for each assessment resample.

skip

A integer indicating how many (if any) additional resamples to skip to thin the total amount of data points in the analysis resample. See the example below.

lag

A value to include an lag between the assessment and analysis set. This is useful if lagged predictors will be used during training and testing.

cumulative

A logical. Should the analysis resample grow beyond the size specified by initial at each resample?.

slice_limit

The number of slices to return. Set to dplyr::n(), which returns the maximum number of slices.

...

Not currently used.

Value

An tibble with classes time_series_cv, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and a column called id that has a character string with the resample identifier.

Details

Time-Based Specification

The initial, assess, skip, and lag variables can be specified as:

• Numeric: initial = 24

• Time-Based Phrases: initial = "2 years", if the data contains a date_var (date or datetime)

Initial (Training Set) and Assess (Testing Set)

The main options, initial and assess, control the number of data points from the original data that are in the analysis (training set) and the assessment (testing set), respectively.

Skip

skip enables the function to not use every data point in the resamples. When skip = 1, the resampling data sets will increment by one position.

Example: Suppose that the rows of a data set are consecutive days. Using skip = 7 will make the analysis data set operate on weeks instead of days. The assessment set size is not affected by this option.

Lag

The Lag parameter creates an overlap between the Testing set. This is needed when lagged predictors are used.

Cumulative vs Sliding Window

When cumulative = TRUE, the initial parameter is ignored and the analysis (training) set will grow as resampling continues while the assessment (testing) set size will always remain static.

When cumulative = FALSE, the initial parameter fixes the analysis (training) set and resampling occurs over a fixed window.

Slice Limit

This controls the number of slices. If slice_limit = 5, only the most recent 5 slices will be returned.

See Also

• time_series_cv() and rsample::rolling_origin() - Functions used to create time series resample specifications.

• plot_time_series_cv_plan() - The plotting function used for visualizing the time series resample plan.

• time_series_split() - A convenience function to return a single time series split containing a training/testing sample.

Examples

# NOT RUN {
library(tidyverse)
library(timetk)

# DATA ----
m750 <- m4_monthly %>% filter(id == "M750")

m750 %>% plot_time_series(date, value)

# RESAMPLE SPEC ----
resample_spec <- time_series_cv(data = m750,
                                initial     = "6 years",
                                assess      = "24 months",
                                skip        = "24 months",
                                cumulative  = FALSE,
                                slice_limit = 3)

resample_spec

# VISUALIZE CV PLAN ----

# Select date and value columns from the tscv diagnostic tool
resample_spec %>% tk_time_series_cv_plan()

# Plot the date and value columns to see the CV Plan
resample_spec %>% plot_time_series_cv_plan(date, value, .interactive = FALSE)

# }