prep_nested: Prepared Nested Modeltime Data

In modeltime: The Tidymodels Extension for Time Series Modeling

Prepared Nested Modeltime Data

Description

A set of functions to simplify preparation of nested data for iterative (nested) forecasting with Nested Modeltime Tables.

Usage

extend_timeseries(.data, .id_var, .date_var, .length_future, ...)

nest_timeseries(.data, .id_var, .length_future, .length_actual = NULL)

split_nested_timeseries(.data, .length_test, .length_train = NULL, ...)

Arguments

.data	A data frame or tibble containing time series data. The data should have: identifier (.id_var): Identifying one or more time series groups date variable (.date_var): A date or date time column target variable (.value): A column containing numeric values that is to be forecasted
.id_var	An id column
.date_var	A date or datetime column
.length_future	Varies based on the function: extend_timeseries(): Defines how far into the future to extend the time series by each time series group. nest_timeseries(): Defines which observations should be split into the .future_data.
...	Additional arguments passed to the helper function. See details.
.length_actual	Can be used to slice the .actual_data to a most recent number of observations.
.length_test	Defines the length of the test split for evaluation.
.length_train	Defines the length of the training split for evaluation.

Details

Preparation of nested time series follows a 3-Step Process:

Step 1: Extend the Time Series

extend_timeseries(): A wrapper for timetk::future_frame() that extends a time series group-wise into the future.

• The group column is specified by .id_var.

• The date column is specified by .date_var.

• The length into the future is specified with .length_future.

• The ... are additional parameters that can be passed to timetk::future_frame()

Step 2: Nest the Time Series

nest_timeseries(): A helper for nesting your data into .actual_data and .future_data.

• The group column is specified by .id_var

• The .length_future defines the length of the .future_data.

• The remaining data is converted to the .actual_data.

• The .length_actual can be used to slice the .actual_data to a most recent number of observations.

The result is a "nested data frame".

Step 3: Split the Actual Data into Train/Test Splits

split_nested_timeseries(): A wrapper for timetk::time_series_split() that generates training/testing splits from the .actual_data column.

• The .length_test is the primary argument that identifies the size of the testing sample. This is typically the same size as the .future_data.

• The .length_train is an optional size of the training data.

• The ... (dots) are additional arguments that can be passed to timetk::time_series_split().

Helpers

extract_nested_train_split() and extract_nested_test_split() are used to simplify extracting the training and testing data from the actual data. This can be helpful when making preprocessing recipes using the recipes package.

Examples

library(dplyr)
library(timetk)


nested_data_tbl <- walmart_sales_weekly %>%
    select(id, date = Date, value = Weekly_Sales) %>%

    # Step 1: Extends the time series by id
    extend_timeseries(
        .id_var     = id,
        .date_var   = date,
        .length_future = 52
    ) %>%

    # Step 2: Nests the time series into .actual_data and .future_data
    nest_timeseries(
        .id_var     = id,
        .length_future = 52
    ) %>%

    # Step 3: Adds a column .splits that contains training/testing indices
    split_nested_timeseries(
        .length_test = 52
    )

nested_data_tbl

# Helpers: Getting the Train/Test Sets
extract_nested_train_split(nested_data_tbl, .row_id = 1)

modeltime documentation built on Oct. 23, 2024, 1:07 a.m.