modeltime 0.4.1

Fixes

modeltime 0.4.0

New Functions

Breaking Changes

modeltime 0.3.1

as_modeltime_table(): New function to convert one or more fitted models stored in a list to a Modeltime Table.

Bug Fixes

modeltime 0.3.0

Panel Data

modeltime_forecast() upgrades:

modeltime_calibrate(): Can now handle panel data.

modeltime_accuracy(): Can now handle panel data.

plot_modeltime_forecast(): Can handle panel data provided the data is grouped by an ID column prior to plotting.

Error Messaging

Compatibility

modeltime 0.2.1

Ensembles

modeltime 0.2.0

Ensembles

New Workflow Helper Functions

Improvements

Data Sets

Modeltime now includes 4 new data sets:

Bug Fix

modeltime 0.1.0

New Features

Forecast without Calibration/Refitting

Sometimes it’s important to make fast forecasts without calculating out-of-sample accuracy and refitting (which requires 2 rounds of model training). You can now bypass the modeltime_calibrate() and modeltime_refit() steps and jump straight into forecasting the future. Here’s an example with h = "3 years". Note that you will not get confidence intervals with this approach because calibration data is needed for this.

# Make forecasts without calibration/refitting (No Confidence Intervals)
# - This assumes the models have been trained on m750
modeltime_table(
    model_fit_prophet,
    model_fit_lm
) %>%
    modeltime_forecast(
        h = "3 years",
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(.conf_interval_show = F)

Residual Analysis & Diagonstics

A common tool when forecasting and analyzing residuals, where residuals are .resid = .actual - .prediction. The residuals may have autocorrelation or nonzero mean, which can indicate model improvement opportunities. In addition, users may which to inspect in-sample and out-of-sample residuals, which can display different results.

New Models

TBATS Model

Use seasonal_reg() and set engine to “tbats”.

seasonal_reg(
    seasonal_period_1 = "1 day",
    seasonal_period_2 = "1 week"
) %>% 
    set_engine("tbats")

NNETAR Model

Use nnetar_reg() and set engine to “nnetar”.

model_fit_nnetar <- nnetar_reg() %>%
    set_engine("nnetar") 

Prophet Model - Logistic Growth Support

New Workflow Helper Functions

Improvements

Bug Fixes

Breaking Changes

modeltime 0.0.2

Confidence Interval Estimation

Fixes

modeltime 0.0.1