modeltime_calibrate: Preparation for forecasting

View source: R/modeltime-calibrate.R

modeltime_calibrateR Documentation

Preparation for forecasting

Description

Calibration sets the stage for accuracy and forecast confidence by computing predictions and residuals from out of sample data.

Usage

modeltime_calibrate(object, new_data, id = NULL, quiet = TRUE, ...)

Arguments

object

A fitted model object that is either:

  1. A modeltime table that has been created using modeltime_table()

  2. A workflow that has been fit by fit.workflow() or

  3. A parsnip model that has been fit using fit.model_spec()

new_data

A test data set tibble containing future information (timestamps and actual values).

id

A quoted column name containing an identifier column identifying time series that are grouped.

quiet

Hide errors (TRUE, the default), or display them as they occur?

...

Additional arguments passed to modeltime_forecast().

Details

The results of calibration are used for:

  • Forecast Confidence Interval Estimation: The out of sample residual data is used to calculate the confidence interval. Refer to modeltime_forecast().

  • Accuracy Calculations: The out of sample actual and prediction values are used to calculate performance metrics. Refer to modeltime_accuracy()

The calibration steps include:

  1. If not a Modeltime Table, objects are converted to Modeltime Tables internally

  2. Two Columns are added:

  • .type: Indicates the sample type. This is:

    • "Test" if predicted, or

    • "Fitted" if residuals were stored during modeling.

  • .calibration_data:

    • Contains a tibble with Timestamps, Actual Values, Predictions and Residuals calculated from new_data (Test Data)

    • If id is provided, will contain a 5th column that is the identifier variable.

Value

A Modeltime Table (mdl_time_tbl) with nested .calibration_data added

Examples

library(tidyverse)
library(lubridate)
library(timetk)
library(parsnip)
library(rsample)
library(modeltime)

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

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)

# --- MODELS ---

# Model 1: prophet ----
model_fit_prophet <- prophet_reg() %>%
    set_engine(engine = "prophet") %>%
    fit(value ~ date, data = training(splits))


# ---- MODELTIME TABLE ----

models_tbl <- modeltime_table(
    model_fit_prophet
)

# ---- CALIBRATE ----

calibration_tbl <- models_tbl %>%
    modeltime_calibrate(
        new_data = testing(splits)
    )

# ---- ACCURACY ----

calibration_tbl %>%
    modeltime_accuracy()

# ---- FORECAST ----

calibration_tbl %>%
    modeltime_forecast(
        new_data    = testing(splits),
        actual_data = m750
    )



modeltime documentation built on June 8, 2022, 1:07 a.m.