modeltime (version 0.4.0)

recursive: Create a Recursive Time Series Model from a Parsnip or Workflow Regression Model

Description

Create a Recursive Time Series Model from a Parsnip or Workflow Regression Model

Usage

recursive(object, transform, train_tail, ...)

Arguments

object

An object of model_fit or a fitted workflow class

transform

A transformation performed on new_data after each step of recursive algorithm. It can be an object of types:

• Method 1, recipe: A recipe generates lagged or sliding features (see examples)

• Method 2, function: Must have one argument data (see examples)

train_tail

A tibble with tail of training data set. In most cases it'll be required to create some variables based on dependent variable.

...

Not currently used.

Value

An object with added recursive class

Details

Recursive model can be used if some of the features used for training is based of dependent variable we already are trying to forecast. Typically, among these features we can find lags (e.g. created with step_lag()) or variables crated with sliding window.

When producing forecast, the following steps are performed:

1. Computing forecast for first row of new data. The first row cannot contain NA in any required column.

2. Filling i-th place of the dependent variable column with already computed forecast.

3. Computing missing features for next step, based on already calculated prediction. These features are computed with on a tibble object made from binded train_tail (i.e. tail of training data set) and new_data (which is an argument of predict function).

4. Jumping into point 2., and repeating rest of steps till the for-loop is ended.

Examples

# NOT RUN {
# Libraries & Setup ----
library(modeltime)
library(tidymodels)
library(tidyverse)
library(lubridate)
library(timetk)
library(slider)

m750

FORECAST_HORIZON <- 24

m750_extended <- m750 %>%
    group_by(id) %>%
    future_frame(
        .length_out = FORECAST_HORIZON,
        .bind_data  = TRUE
    ) %>%
    ungroup()

# METHOD 1: RECIPE ----
# - Used for recursive transformations via recipe prepeocessing steps

# Lag Recipe
recipe_lag <- recipe(value ~ date, m750_extended) %>%
    step_lag(value, lag = 1:FORECAST_HORIZON)

# Data Preparation
m750_lagged <- recipe_lag %>% prep() %>% juice()

m750_lagged

train_data <- m750_lagged %>%
    filter(!is.na(value)) %>%
    drop_na()

future_data <- m750_lagged %>%
    filter(is.na(value))

# Modeling
model_fit_lm <- linear_reg() %>%
    set_engine("lm") %>%
    fit(value ~ date, data = train_data)

model_fit_lm_recursive <- linear_reg() %>%
    set_engine("lm") %>%
    fit(value ~ ., data = train_data) %>%
    recursive(
        transform  = recipe_lag,
        train_tail = tail(train_data, FORECAST_HORIZON)
    )

model_fit_lm_recursive

# Forecasting
modeltime_table(
    model_fit_lm,
    model_fit_lm_recursive
) %>%
    update_model_description(2, "LM - Lag Roll") %>%
    modeltime_forecast(
        new_data    = future_data,
        actual_data = m750,
        keep_data   = TRUE
    ) %>%
    plot_modeltime_forecast(
        .interactive        = FALSE,
        .conf_interval_show = FALSE
    )


# METHOD 2: TRANSFORM FUNCTION ----
# - Used for complex transformations via transformation function

# Function run recursively that updates the forecasted dataset
lag_roll_transformer <- function(data){
    data %>%
        # Lags
        tk_augment_lags(value, .lags = 1:12) %>%
        # Rolling Features
        mutate(rolling_mean_12 = lag(slide_dbl(
            value, .f = mean, .before = 12, .complete = FALSE
        ), 1))
}

# Data Preparation
m750_rolling <- m750_extended %>%
    lag_roll_transformer() %>%
    select(-id)

train_data <- m750_rolling %>%
    filter(!is.na(value)) %>%
    drop_na()

future_data <- m750_rolling %>%
    filter(is.na(value))

# Modeling
model_fit_lm <- linear_reg() %>%
    set_engine("lm") %>%
    fit(value ~ date, data = train_data)

model_fit_lm_recursive <- linear_reg() %>%
    set_engine("lm") %>%
    fit(value ~ ., data = train_data) %>%
    recursive(
        transform  = lag_roll_transformer,
        train_tail = tail(train_data, FORECAST_HORIZON)
    )

model_fit_lm_recursive

# Forecasting
modeltime_table(
    model_fit_lm,
    model_fit_lm_recursive
) %>%
    update_model_description(2, "LM - Lag Roll") %>%
    modeltime_forecast(
        new_data    = future_data,
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(
        .interactive        = FALSE,
        .conf_interval_show = FALSE
    )


# }