modeltime_forecast: Forecast future data

View source: R/modeltime-forecast.R

modeltime_forecastR Documentation

Forecast future data


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


  new_data = NULL,
  h = NULL,
  actual_data = NULL,
  conf_interval = 0.95,
  conf_by_id = FALSE,
  conf_method = "conformal_default",
  keep_data = FALSE,
  arrange_index = FALSE,



A Modeltime Table


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


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.


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


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


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().


Algorithm used to produce confidence intervals. All CI's are Conformal Predictions. Choose one of:

  • conformal_default: Uses qnorm() to compute quantiles from out-of-sample (test set) residuals.

  • conformal_split: Uses the split method split conformal inference method described by Lei et al (2018)


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.


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


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.

  • Forecasting Evaluation Data: By default, the new_data will use the .calibration_data if new_data is not provided. This is the equivalent of using rsample::testing() for getting test data sets.

  • Forecasting Future Data: See timetk::future_frame() for creating future tibbles.

  • Xregs: Can be used with this method

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.

  • Forecasting Future Data: All forecasts using h are extended after the calibration data or actual_data.

  • Extending .calibration_data - Calibration data is given 1st priority, which is desirable after refitting with modeltime_refit(). Internally, a call is made to timetk::future_frame() to expedite creating new data using the date feature.

  • Extending actual_data - If h is provided, and the modeltime table has not been calibrated, the "actual_data" will be extended into the future. This is useful in situations where you want to go directly from modeltime_table() to modeltime_forecast() without calibrating or refitting.

  • Xregs: Cannot be used because future data must include new xregs. If xregs are desired, build a future data frame and use new_data.

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. The algorithm used to produce confidence intervals can be changed with the conf_method parameter.

Conformal Default Method:

When conf_method = "conformal_default" (default), this method uses qnorm() to produce a 95% confidence interval by default. It estimates a normal (Gaussian distribution) based on the out-of-sample errors (residuals).

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.

Conformal Split Method:

When ⁠conf_method = "conformal_split⁠, this method uses the split conformal inference method described by Lei et al (2018). This is also implemented in the probably R package's int_conformal_split() function.

What happens to the confidence interval after refitting models?

Refitting has no affect on the confidence interval since this is calculated independently of the refitted model. 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.


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

  • .key: Values labeled either "prediction" or "actual"

  • .index: The timestamp index.

  • .value: The value being forecasted.

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

  • .conf_lo: The lower limit of the confidence interval.

  • .conf_hi: The upper limit of the confidence interval.

Additional descriptive columns are included:

  • .model_id: Model ID from the Modeltime Table

  • .model_desc: Model Description from the Modeltime Table

Unnecessary columns are dropped to save space:

  • .model

  • .calibration_data


Lei, Jing, et al. "Distribution-free predictive inference for regression." Journal of the American Statistical Association 113.523 (2018): 1094-1111.



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

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

# --- MODELS ---

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


models_tbl <- modeltime_table(

# ---- CALIBRATE ----

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

# ---- ACCURACY ----

calibration_tbl %>%


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

# Skips Calibration Step, No Confidence Intervals

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

# Keeps the new data. Useful if new data has information
#  like ID features that should be kept with the forecast data

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

modeltime documentation built on Sept. 2, 2023, 5:06 p.m.