modeltime_forecast: Forecast future data

Description Usage Arguments Details Value Examples

View source: R/modeltime-forecast.R

Description

The goal of modeltime_forecast() is to simplify the process of forecasting future data.

Usage

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modeltime_forecast(
  object,
  new_data = NULL,
  h = NULL,
  actual_data = NULL,
  conf_interval = 0.95,
  conf_by_id = FALSE,
  keep_data = FALSE,
  arrange_index = FALSE,
  ...
)

Arguments

object

A Modeltime Table

new_data

A tibble containing future information to forecast. If NULL, forecasts the calibration data.

h

The forecast horizon (can be used instead of new_data for time series with no exogenous regressors). Extends the calibration data h periods into the future.

actual_data

Reference data that is combined with the output tibble and given a .key = "actual"

conf_interval

An estimated confidence interval based on the calibration data. This is designed to estimate future confidence from out-of-sample prediction error.

conf_by_id

Whether or not to produce confidence interval estimates by an ID feature.

  • When FALSE, a global model confidence interval is provided.

  • If TRUE, a local confidence interval is provided group-wise for each time series ID. To enable local confidence interval, an id must be provided during modeltime_calibrate().

keep_data

Whether or not to keep the new_data and actual_data as extra columns in the results. This can be useful if there is an important feature in the new_data and actual_data needed when forecasting. Default: FALSE.

arrange_index

Whether or not to sort the index in rowwise chronological order (oldest to newest) or to keep the original order of the data. Default: FALSE.

...

Not currently used

Details

The modeltime_forecast() function prepares a forecast for visualization with with plot_modeltime_forecast(). The forecast is controlled by new_data or h, which can be combined with existing data (controlled by actual_data). Confidence intervals are included if the incoming Modeltime Table has been calibrated using modeltime_calibrate(). Otherwise confidence intervals are not estimated.

New Data

When forecasting you can specify future data using new_data. This is a future tibble with date column and columns for xregs extending the trained dates and exogonous regressors (xregs) if used.

H (Horizon)

When forecasting, you can specify h. This is a phrase like "1 year", which extends the .calibration_data (1st priority) or the actual_data (2nd priority) into the future.

Actual Data

This is reference data that contains the true values of the time-stamp data. It helps in visualizing the performance of the forecast vs the actual data.

When h is used and the Modeltime Table has not been calibrated, then the actual data is extended into the future periods that are defined by h.

Confidence Interval Estimation

Confidence intervals (.conf_lo, .conf_hi) are estimated based on the normal estimation of the testing errors (out of sample) from modeltime_calibrate(). The out-of-sample error estimates are then carried through and applied to applied to any future forecasts.

The confidence interval can be adjusted with the conf_interval parameter. An 80% confidence interval estimates a normal (Gaussian distribution) that assumes that 80% of the future data will fall within the upper and lower confidence limits.

The confidence interval is mean-adjusted, meaning that if the mean of the residuals is non-zero, the confidence interval is adjusted to widen the interval to capture the difference in means.

Refitting has no affect on the confidence interval since this is calculated independently of the refitted model (on data with a smaller sample size). New observations typically improve future accuracy, which in most cases makes the out-of-sample confidence intervals conservative.

Keep Data

Include the new data (and actual data) as extra columns with the results of the model forecasts. This can be helpful when the new data includes information useful to the forecasts. An example is when forecasting Panel Data and the new data contains ID features related to the time series group that the forecast belongs to.

Arrange Index

By default, modeltime_forecast() keeps the original order of the data. If desired, the user can sort the output by .key, .model_id and .index.

Value

A tibble with predictions and time-stamp data. For ease of plotting and calculations, the column names are transformed to:

Additionally, if the Modeltime Table has been previously calibrated using modeltime_calibrate(), you will gain confidence intervals.

Additional descriptive columns are included:

Unnecessary columns are dropped to save space:

Examples

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

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

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

# --- MODELS ---

# Model 1: auto_arima ----
model_fit_arima <- arima_reg() %>%
    set_engine(engine = "auto_arima") %>%
    fit(value ~ date, data = training(splits))


# ---- MODELTIME TABLE ----

models_tbl <- modeltime_table(
    model_fit_arima
)

# ---- CALIBRATE ----

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

# ---- ACCURACY ----

calibration_tbl %>%
    modeltime_accuracy()

# ---- FUTURE FORECAST ----

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

# ---- ALTERNATIVE: FORECAST WITHOUT CONFIDENCE INTERVALS ----
# Skips Calibration Step, No Confidence Intervals

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

# ---- KEEP NEW DATA WITH FORECAST ----
# Keeps the new data. Useful if new data has information
#  like ID features that should be kept with the forecast data

calibration_tbl %>%
    modeltime_forecast(
        new_data      = testing(splits),
        keep_data     = TRUE
    )

modeltime documentation built on July 16, 2021, 9:08 a.m.