hardhat 1.0.0 #182plot_modeltime_forecast(): Expose the
facet_trelliscope() plotting parameters.step_rm() to get rid of date rather than updating
its role #181New Features
Many of the plotting functions have been upgraded for use with
trelliscopejs for easier visualization of many time
series.
plot_modeltime_forecast():
trelliscope: Used for visualizing
many time series..facet_strip_remove to remove facet
strips since trelliscope is automatically labeled..facet_nrow to adjust grid with
trelliscope.facet_collapse = TRUE was
changed to FALSE for better compatibility with Trelliscope
JS. This may cause some plots to have multiple groups take up extra
space in the strip.Modeltime now has a Spark Backend
NEW Vignette - Modeltime Spark Backend describing how to set up Modeltime with the Spark Backend.
If users install smooth, the following models become
available:
adam_reg(): Interfaces with the ADAM forecasting
algorithm in smooth.
exp_smoothing(): A new engine “smooth_es” connects
to the Exponential Smoothing algorithm in smooth::es().
This algorithm has several advantages, most importantly that it can use
x-regs (unlike “ets” engine).
extract_nested_modeltime_table() -
Extracts a nested modeltime table by row id.extract_nested_train_split and
extract_nested_test_split: Changed parameter from
.data to .object for consistency with other
“extract” functions
Added a new logged feature to modeltime_nested_fit()
to track the attribute “metric_set”, which is needed for ensembles. Old
nested modeltime objects will need to be re-run to get this new
attribute. This will be used in ensembles.
Nested (Iterative) Forecasting is aimed at making it easier to perform forecasting that is traditionally done in a for-loop with models like ARIMA, Prophet, and Exponential Smoothing. Functionality has been added to:
extend_timeseries(), nest_timeseries(), and
split_nested_timeseris().modeltime_nested_fit(): Fits many
models to nested time series data and organizes in a “Nested Modeltime
Table”. Logs Accuracy, Errors, and Test Forecasts.
control_nested_fit(): Used to
control the fitting process including verbosity and parallel
processing.
Logging Extractors: Functions that retrieve
logged information from the initial fitting process.
extract_nested_test_accuracy(),
extract_nested_error_report(), and
extract_nested_test_forecast().
modeltime_nested_select_best():
Selects the best model for each time series ID.
Logging Extractors: Functions that retrieve
logged information from the model selection process.
extract_nested_best_model_report()
modeltime_nested_refit(): Refits to
the .future_data. Logs Future Forecasts.
control_nested_refit(): Used to
control the re-fitting process including verbosity and parallel
processing.
Logging Extractors: Functions that retrieve
logged information from the re-fitting process.
extract_nested_future_forecast().
extended_forecast_accuracy_metric_set(): Adds the new
MAAPE metric for handling intermittent data when MAPE returns Inf.maape(): New yardstick metric that calculates “Mean
Arctangent Absolute Percentage Error” (MAAPE). Used when MAPE returns
Inf typically due to intermittent data.modeltime_fit_workflowset(): Improved handling of
Workflowset Descriptions, which now match the
wflow_id.We’ve expanded Panel Data functionality to produce model accuracy and confidence interval estimates by a Time Series ID (#114). This is useful when you have a Global Model that produces forecasts for more than one time series. You can more easily obtain grouped accuracy and confidence interval estimates.
modeltime_calibrate(): Gains an id
argument that is a quoted column name. This identifies that the
residuals should be tracked by an time series identifier feature that
indicates the time series groups.
modeltime_accuracy(): Gains a acc_by_id
argument that is TRUE/FALSE. If the data has
been calibrated with id, then the user can return local
model accuracy by the identifier column. The accuracy data frame will
return a row for each combination of Model ID and Time Series
ID.
modeltime_forecast(): Gains a
conf_by_id argument that is
TRUE/FALSE. If the data has been calibrated
with id, then the user can return local model confidence by
the identifier column. The forecast data frame will return an extra
column indicating the identifier column. The confidence intervals will
be adjusted based on the local time series ID variance instead of the
global model variance.
temporal_hierarchy(): Implements the thief
package by Rob Hyndman and Nikolaos Kourentzes for “Temporal
HIErarchical Forecasting”. #117modeltime_fit_workflowset()
where the workflowset (wflw_id) order was not maintained.Parallel Processing
New Vignette: Parallel Processing
parallel_start() and parallel_stop():
Helpers for setting up multicore processing.
create_model_grid(): Helper to generate model
specifications with filled-in parameters from a parameter grid
(e.g. dials::grid_regular()).
control_refit() and
control_fit_workflowset(): Better printing.
Bug Fixes
cores > cores_available.modeltime_fit_workflowset() (#85) makes it easy to
convert workflow_set objects to Modeltime Tables
(mdl_time_tbl). Requires a refitting process that can now
be performed in parallel or in sequence.
exp_smoothing().exp_smoothing().exp_smoothing() gained 3 new tunable parameters:
smooth_level(): This is often called the “alpha”
parameter used as the base level smoothing factor for exponential
smoothing models.smooth_trend(): This is often called the “beta”
parameter used as the trend smoothing factor for exponential smoothing
models.smooth_seasonal(): This is often called the “gamma”
parameter used as the seasonal smoothing factor for exponential
smoothing models.modeltime_refit(): supports parallel processing. See
control_refit()modeltime_fit_workflowset(): supports parallel
processing. See control_workflowset()boost_tree(mtry): Mapping switched from
colsample_bytree to colsample_bynode.
prophet_boost() and arima_boost() have been
updated to reflect this change.
https://github.com/tidymodels/parsnip/pull/499exp_smoothing() models produced in prior versions may
require refitting with modeltime_refit() to upgrade their
internals with the new parameters.recursive() for ensembles. The new
recursive ensemble functionality is in modeltime.ensemble
>= 0.3.0.9000.recursive() (#71) - Received a full upgrade to work
with Panel Data.modeltime::metric_tweak() for
yardstick::metric_tweak(). The
yardstick::metric_tweak() has a required .name
argument in addition to .fn, which is needed for
tuning.Baseline algorithms (#5, #37) have been created for comparing high-performance methods with simple forecasting methods.
window_reg: Window-based methods such as mean, median,
and even more complex seasonal models based on a forecasting window. The
main tuning parameter is window_size.naive_reg: NAIVE and Seasonal NAIVE (SNAIVE) Regression
Modelsmetric_tweak() - Can modify yardstick
metrics like mase(), which have seasonal parameters.default_forecast_accuracy_metric_set() - Gets a
... parameter that allows us to add more metrics beyond the
defaults.A new function is added modeltime_residuals_test() (#62,
#68). Tests are implemented:
plot_modeltime_forecast() - When plotting a single
point forecast, plot_modeltime_forecast() now uses
geom_point() instead of geom_line(). Fixes
#66.Fixes
recursive() & modeltime_refit(): Now
able to refit a recursive workflow or recursive fitted parsnip
object.New Functions
recursive(): Turn a fitted model into a recursive
predictor. (#49, #50)update_modeltime_model(): New function to update a
modeltime model inside a Modeltime Table.Breaking Changes
arima_workflow_tuned dataset.as_modeltime_table(): New function to convert one or
more fitted models stored in a list to a Modeltime
Table.
Bug Fixes
m750_models: Fixes error “R parsnip Error:
Internal error: Unknown composition type.”Panel Data
modeltime_forecast() upgrades:
keep_data: Gains a new argument keep_data.
This is useful when the new_data and
actual_data has important information needed in analyzing
the forecast.arrange_index: Gains a new argument
arrange_index. By default, the forecast keeps the rows in
the same order as the incoming data. Prior versions arranged Model
Predictions by .index, which impacts the users ability to
match to Panel Data which is not likely to be arranged by date.
Prediction best-practices are to keep the original order of the data,
which will be preserved by default. To get the old behavior, simply
toggle arrange_index = TRUE.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
modeltime_calibrate(quiet = FALSE).Compatibility
parsnip >= 0.1.4. Uses
set_encodings() new parameter
allow_sparse_x.Ensembles
modeltime_refit() - Changes to improve fault tolerance
and error handling / messaging when making ensembles.Ensembles
modeltime.ensemble, a new R package designed
for forecasting with ensemble models.New Workflow Helper Functions
add_modeltime_model() - A helper function making it
easy to add a fitted parsnip or workflow object to a modeltime
tablepluck_modeltime_model() &
pull_modeltime_model() - A helper function making it easy
to extract a model from a modeltime tableImprovements
?prophet_boostprophet_reg() can now have regressors controlled via
set_engine() using the following parameters:
regressors.mode - Set to seasonality.mode
by default.regressors.prior.scale - Set to 10,000 by default.regressors.standardize - Set to “auto” by default.Data Sets
Modeltime now includes 4 new data sets:
m750 - M750 Time Series Datasetm750_models - 3 Modeltime Models made on the M750
Datasetm750_splits - An rsplit object containing
Train/test splits of the M750 datam750_training_resamples - A Time Series Cross
Validation time_series_cv object made from the
training(m750_splits)Bug Fix
plot_modeltime_forecast() fix issue with “ACTUAL” data
being shown at bottom of legend list. Should be first item.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.
modeltime_residuals() - A new function used to extract
out residual informationplot_modeltime_residuals() - Visualizes the output of
modeltime_residuals(). Offers 3 plots:
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
prophet_reg() and prophet_boost():
growth = 'logistic'
and one or more of logistic_cap and
logistic_floor to valid saturation boundaries.changepoint_num, changepoint_range,
seasonality_yearly, seasonality_weekly,
seasonality_daily, logistic_cap,
logistic_floorcombine_modeltime_tables() - A helper function making
it easy to combine multiple modeltime tables.update_model_description() - A helper function making
it easier to update model descriptions.modeltime_refit(): When modeltime model parameters
update (e.g. when Auto ARIMA changes to a new model), the Model
Description now alerts the user (e.g. “UPDATE:
ARIMA(0,1,1)(1,1,1)[12]”).
modeltime_calibrate(): When training data is
supplied in a time window that the model has previously been trained on
(e.g. training(splits)), the calibration calculation first
inspects whether the “Fitted” data exists. If it iexists, it returns the
“Fitted” data. This helps prevent sequence-based (e.g. ARIMA, ETS, TBATS
models) from displaying odd results because these algorithms can only
predict sequences directly following the training window. If “Fitted”
data is being used, the .type column will display “Fitted”
instead of “Test”.
modeltime_forecast():
actual_data reconciliation strategies when
recipe removes rows. Strategy attempts to fill predictors using “downup”
strategy to prevent NA values from removing rows.modeltime_accuracy(): Fix issue with
new_data not recalibrating.
prophet_reg() and prophet_boost() - Can
now perform logistic growth growth = 'logistic'. The user
can supply “saturation” bounds using logistic_cap and/or
logisitc_floor.
seasonal_decomp() has changed to
seasonal_reg() and now supports both TBATS and Seasonal
Decomposition Models.prophet_reg() & prophet_boost():
Argument changes:
num_changepoints has become
changepoint_nummodeltime_forecast(): Now estimates confidence
intervals using centered standard deviation. The mean is assumed to be
zero and residuals deviate from mean = 0.parsnip 0.1.2.prophet_boost(): Set nthreads = 1
(default) to ensure parallelization is thread safe.