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Forecasting with tidymodels
made easy! This short
tutorial shows how you can use:
arima_reg()
,
arima_boost()
, exp_smoothing()
,
prophet_reg()
, prophet_boost()
, and morelinear_reg()
,
mars()
, svm_rbf()
, rand_forest()
,
boost_tree()
and more…to perform classical time series analysis and machine learning
in one framework! See “Model
List” for the full list of modeltime
models.
For those that prefer video tutorials, we have an 11-minute YouTube Video that walks you through the Modeltime Workflow.
(Click to Watch on YouTube)
Here’s the general process and where the functions fit.
Just follow the modeltime
workflow, which is detailed in
6 convenient steps:
Let’s go through a guided tour to kick the tires on
modeltime
.
Load libraries to complete this short tutorial.
library(xgboost)
library(tidymodels)
library(modeltime)
library(tidyverse)
library(timetk)
# This toggles plots from plotly (interactive) to ggplot (static)
interactive <- FALSE
We can visualize the dataset.
Let’s split the data into training and test sets using
initial_time_split()
We can easily create dozens of forecasting models by combining
modeltime
and parsnip
. We can also use the
workflows
interface for adding preprocessing! Your
forecasting possibilities are endless. Let’s get a few basic models
developed:
Important note: Handling Date Features
Modeltime models (e.g. arima_reg()
) are created
with a date or date time feature in the model. You will see that most
models include a formula like fit(value ~ date, data)
.
Parsnip models (e.g. linear_reg()
) typically
should not have date features, but may contain derivatives of dates
(e.g. month, year, etc). You will often see formulas like
fit(value ~ as.numeric(date) + month(date), data)
.
First, we create a basic univariate ARIMA model using “Auto Arima”
using arima_reg()
Next, we create a boosted ARIMA using arima_boost()
.
Boosting uses XGBoost to model the ARIMA errors. Note that model formula
contains both a date feature and derivatives of date - ARIMA uses the
date - XGBoost uses the derivatives of date as regressors
Normally I’d use a preprocessing workflow for the month features
using a function like step_timeseries_signature()
from
timetk
to help reduce the complexity of the parsnip formula
interface.
# Model 2: arima_boost ----
model_fit_arima_boosted <- arima_boost(
min_n = 2,
learn_rate = 0.015
) %>%
set_engine(engine = "auto_arima_xgboost") %>%
fit(value ~ date + as.numeric(date) + factor(month(date, label = TRUE), ordered = F),
data = training(splits))
#> frequency = 12 observations per 1 year
Next, create an Error-Trend-Season (ETS) model using an Exponential
Smoothing State Space model. This is accomplished with
exp_smoothing()
.
We’ll create a prophet
model using
prophet_reg()
.
# Model 4: prophet ----
model_fit_prophet <- prophet_reg() %>%
set_engine(engine = "prophet") %>%
fit(value ~ date, data = training(splits))
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
We can model time series linear regression (TSLM) using the
linear_reg()
algorithm from parsnip
. The
following derivatives of date are used:
as.numeric(date)
month(date)
We can model a Multivariate Adaptive Regression Spline model using
mars()
. I’ve modified the process to use a
workflow
to standardize the preprocessing of the features
that are provided to the machine learning model (mars).
# Model 6: earth ----
model_spec_mars <- mars(mode = "regression") %>%
set_engine("earth")
recipe_spec <- recipe(value ~ date, data = training(splits)) %>%
step_date(date, features = "month", ordinal = FALSE) %>%
step_mutate(date_num = as.numeric(date)) %>%
step_normalize(date_num) %>%
step_rm(date)
wflw_fit_mars <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(model_spec_mars) %>%
fit(training(splits))
#>
#> Attaching package: 'plotrix'
#> The following object is masked from 'package:scales':
#>
#> rescale
OK, with these 6 models, we’ll show how easy it is to forecast.
The next step is to add each of the models to a Modeltime Table using
modeltime_table()
. This step does some basic checking to
make sure each of the models are fitted and that organizes into a
scalable structure called a “Modeltime Table”
that is used as part of our forecasting workflow.
We have 6 models to add. A couple of notes before moving on:
modeltime_table()
will complain (throw an informative
error) saying you need to fit()
the model.models_tbl <- modeltime_table(
model_fit_arima_no_boost,
model_fit_arima_boosted,
model_fit_ets,
model_fit_prophet,
model_fit_lm,
wflw_fit_mars
)
models_tbl
#> # Modeltime Table
#> # A tibble: 6 × 3
#> .model_id .model .model_desc
#> <int> <list> <chr>
#> 1 1 <fit[+]> ARIMA(0,1,1)(0,1,1)[12]
#> 2 2 <fit[+]> ARIMA(0,1,1)(0,1,1)[12] W/ XGBOOST ERRORS
#> 3 3 <fit[+]> ETS(M,A,A)
#> 4 4 <fit[+]> PROPHET
#> 5 5 <fit[+]> LM
#> 6 6 <workflow> EARTH
Calibrating adds a new column, .calibration_data
, with
the test predictions and residuals inside. A few notes on
Calibration:
calibration_tbl <- models_tbl %>%
modeltime_calibrate(new_data = testing(splits))
calibration_tbl
#> # Modeltime Table
#> # A tibble: 6 × 5
#> .model_id .model .model_desc .type .calibration_data
#> <int> <list> <chr> <chr> <list>
#> 1 1 <fit[+]> ARIMA(0,1,1)(0,1,1)[12] Test <tibble [31 × 4]>
#> 2 2 <fit[+]> ARIMA(0,1,1)(0,1,1)[12] W/ XGBOO… Test <tibble [31 × 4]>
#> 3 3 <fit[+]> ETS(M,A,A) Test <tibble [31 × 4]>
#> 4 4 <fit[+]> PROPHET Test <tibble [31 × 4]>
#> 5 5 <fit[+]> LM Test <tibble [31 × 4]>
#> 6 6 <workflow> EARTH Test <tibble [31 × 4]>
There are 2 critical parts to an evaluation.
Visualizing the Test Error is easy to do using the interactive plotly visualization (just toggle the visibility of the models using the Legend).
calibration_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = m750
) %>%
plot_modeltime_forecast(
.legend_max_width = 25, # For mobile screens
.interactive = interactive
)
From visualizing the test set forecast:
We can use modeltime_accuracy()
to collect common
accuracy metrics. The default reports the following metrics using
yardstick
functions:
mae()
mape()
mase()
smape()
rmse()
rsq()
These of course can be customized following the rules for creating
new yardstick metrics, but the defaults are very useful. Refer to
default_forecast_accuracy_metrics()
to learn more.
To make table-creation a bit easier, I’ve included
table_modeltime_accuracy()
for outputing results in either
interactive (reactable
) or static (gt
)
tables.
Accuracy Table | ||||||||
.model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
---|---|---|---|---|---|---|---|---|
1 | ARIMA(0,1,1)(0,1,1)[12] | Test | 151.33 | 1.41 | 0.52 | 1.43 | 197.71 | 0.93 |
2 | ARIMA(0,1,1)(0,1,1)[12] W/ XGBOOST ERRORS | Test | 147.04 | 1.37 | 0.50 | 1.39 | 191.84 | 0.93 |
3 | ETS(M,A,A) | Test | 77.00 | 0.73 | 0.26 | 0.73 | 90.27 | 0.98 |
4 | PROPHET | Test | 177.74 | 1.70 | 0.61 | 1.70 | 234.91 | 0.88 |
5 | LM | Test | 629.12 | 6.01 | 2.15 | 5.81 | 657.19 | 0.91 |
6 | EARTH | Test | 709.83 | 6.59 | 2.42 | 6.86 | 782.82 | 0.55 |
From the accuracy metrics:
The final step is to refit the models to the full dataset using
modeltime_refit()
and forecast them forward.
The models have all changed! (Yes - this is the point of refitting)
This is the (potential) benefit of refitting.
More often than not refitting is a good idea. Refitting:
min_n = 2
,
learn_rate = 0.015
.We just showcased the Modeltime Workflow. But this is a simple problem. And, there’s a lot more to learning time series.
Your probably thinking how am I ever going to learn time series forecasting. Here’s the solution that will save you years of struggling.
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.