Introducing Modeltime: Tidy Time Series Forecasting using Tidymodels
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I’m beyond excited to introduce modeltime
, a new time series forecasting package designed to speed up model evaluation, selection, and forecasting. modeltime
does this by integrating the tidymodels
machine learning ecosystem of packages into a streamlined workflow for tidyverse
forecasting. Follow this article to get started with modeltime
. If you like what you see, I have an Advanced Time Series Course coming soon (join the waitlist) where you will become a timeseries expert for your organization by learning modeltime
and timetk
.
Modeltime
The forecasting framework for the tidymodels ecosystem
modeltime
is a new package designed for rapidly developing and testing time series models using machine learning models, classical models, and automated models. There are three key benefits:

Systematic Workflow for Forecasting. Learn a few key functions like
modeltime_table()
,modeltime_calibrate()
, andmodeltime_refit()
to develop and train time series models. 
Unlocks Tidymodels for Forecasting. Gain the benefit of all or the
parsnip
models includingboost_tree()
(XGBoost, C5.0),linear_reg()
(GLMnet, Stan, Linear Regression),rand_forest()
(Random Forest), and more 
New Time Series Boosted Models including Boosted ARIMA (
arima_boost()
) and Boosted Prophet (prophet_boost()
) that can improve accuracy by applying XGBoost model to the errors
Getting Started
Let’s kick the tires on modeltime
Install modeltime
.
Load the following libraries.
Get Your Data
Forecasting daily bike transactions
We’ll start with a bike_sharing_daily
time series data set that includes bike transactions. We’ll simplify the data set to a univariate time series with columns, “date” and “value”.
Next, visualize the dataset with the plot_time_series()
function. Toggle .interactive = TRUE
to get a plotly interactive plot. FALSE
returns a ggplot2 static plot.
Train / Test
Split your time series into training and testing sets
Next, use time_series_split()
to make a train/test set.
 Setting
assess = "3 months"
tells the function to use the last 3months of data as the testing set.  Setting
cumulative = TRUE
tells the sampling to use all of the prior data as the training set.
Next, visualize the train/test split.
tk_time_series_cv_plan()
: Converts the splits object to a data frameplot_time_series_cv_plan()
: Plots the time series sampling data using the “date” and “value” columns.
Modeling
This is exciting.
Now for the fun part! Let’s make some models using functions from modeltime
and parsnip
.
Automatic Models
Automatic models are generally modeling approaches that have been automated. This includes “Auto ARIMA” and “Auto ETS” functions from forecast
and the “Prophet” algorithm from prophet
. These algorithms have been integrated into modeltime
. The process is simple to set up:
 Model Spec: Use a specification function (e.g.
arima_reg()
,prophet_reg()
) to initialize the algorithm and key parameters  Engine: Set an engine using one of the engines available for the Model Spec.
 Fit Model: Fit the model to the training data
Let’s make several models to see this process in action.
Auto ARIMA
Here’s the basic Auto Arima Model fitting process.
 Model Spec:
arima_reg()
<– This sets up your general model algorithm and key parameters  Set Engine:
set_engine("auto_arima")
<– This selects the specific packagefunction to use and you can add any functionlevel arguments here.  Fit Model:
fit(value ~ date, training(splits))
<– All modeltime models require a date column to be a regressor.
Prophet
Prophet is specified just like Auto ARIMA. Note that I’ve changed to prophet_reg()
, and I’m passing an enginespecific parameter yearly.seasonality = TRUE
using set_engine()
.
Machine Learning Models
Machine learning models are more complex than the automated models. This complexity typically requires a workflow (sometimes called a pipeline in other languages). The general process goes like this:
 Create Preprocessing Recipe
 Create Model Specifications
 Use Workflow to combine Model Spec and Preprocessing, and Fit Model
Preprocessing Recipe
First, I’ll create a preprocessing recipe using recipe()
and adding time series steps. The process uses the “date” column to create 45 new features that I’d like to model. These include timeseries signature features and fourier series.
With a recipe inhand, we can set up our machine learning pipelines.
Elastic Net
Making an Elastic NET model is easy to do. Just set up your model spec using linear_reg()
and set_engine("glmnet")
. Note that we have not fitted the model yet (as we did in previous steps).
Next, make a fitted workflow:
 Start with a
workflow()
 Add a Model Spec:
add_model(model_spec_glmnet)
 Add Preprocessing:
add_recipe(recipe_spec %>% step_rm(date))
<– Note that I’m removing the “date” column since Machine Learning algorithms don’t typically know how to deal with date or datetime features  Fit the Workflow:
fit(training(splits))
Random Forest
We can fit a Random Forest using a similar process as the Elastic Net.
New Hybrid Models
I’ve included several hybrid models (e.g. arima_boost()
and prophet_boost()
) that combine both automated algorithms with machine learning. I’ll showcase prophet_boost()
next!
Prophet Boost
The Prophet Boost algorithm combines Prophet with XGBoost to get the best of both worlds (i.e. Prophet Automation + Machine Learning). The algorithm works by:
 First modeling the univariate series using Prophet
 Using regressors supplied via the preprocessing recipe (remember our recipe generated 45 new features), and regressing the Prophet Residuals with the XGBoost model
We can set the model up using a workflow just like with the machine learning algorithms.
The Modeltime Workflow
Speed up model evaluation and selection with modeltime
The modeltime
workflow is designed to speed up model evaluation and selection. Now that we have several time series models, let’s analyze them and forecast the future with the modeltime
workflow.
Modeltime Table
The Modeltime Table organizes the models with IDs and creates generic descriptions to help us keep track of our models. Let’s add the models to a modeltime_table()
.
Calibration
Model Calibration is used to quantify error and estimate confidence intervals. We’ll perform model calibration on the outofsample data (aka. the Testing Set) with the modeltime_calibrate()
function. Two new columns are generated (“.type” and “.calibration_data”), the most important of which is the “.calibration_data”. This includes the actual values, fitted values, and residuals for the testing set.
Forecast (Testing Set)
With calibrated data, we can visualize the testing predictions (forecast).
 Use
modeltime_forecast()
to generate the forecast data for the testing set as a tibble.  Use
plot_modeltime_forecast()
to visualize the results in interactive and static plot formats.
Accuracy (Testing Set)
Next, calculate the testing accuracy to compare the models.
 Use
modeltime_accuracy()
to generate the outofsample accuracy metrics as a tibble.  Use
table_modeltime_accuracy()
to generate interactive and static
Accuracy Table  

.model_id  .model_desc  .type  mae  mape  mase  smape  rmse  rsq 
1  ARIMA(0,1,3) WITH DRIFT  Test  2540.11  474.89  2.74  46.00  3188.09  0.39 
2  PROPHET  Test  1221.18  365.13  1.32  28.68  1764.93  0.44 
3  GLMNET  Test  1197.06  340.57  1.29  28.44  1650.87  0.49 
4  RANDOMFOREST  Test  1338.15  335.52  1.45  30.63  1855.21  0.46 
5  PROPHET W/ XGBOOST ERRORS  Test  1189.28  332.44  1.28  28.48  1644.25  0.55 
Analyze Results
From the accuracy measures and forecast results, we see that:
 Auto ARIMA model is not a good fit for this data.
 The best model is Prophet + XGBoost
Let’s exclude the Auto ARIMA from our final model, then make future forecasts with the remaining models.
Refit and Forecast Forward
Refitting is a bestpractice before forecasting the future.
modeltime_refit()
: We retrain on full data (bike_transactions_tbl
)modeltime_forecast()
: For models that only depend on the “date” feature, we can useh
(horizon) to forecast forward. Settingh = "12 months"
forecasts then next 12months of data.
It gets better
You’ve just scratched the surface, here’s what’s coming…
The modeltime
package functionality is much more featurerich than what we’ve covered here (I couldn’t possibly cover everything in this post). 😀
Here’s what I didn’t cover:

Feature engineering: The art of time series analysis is feature engineering. Modeltime works with cuttingedge timeseries preprocessing tools including those in
recipes
andtimetk
packages. 
Hyper parameter tuning: ARIMA models and Machine Learning models can be tuned. There’s a right and a wrong way (and it’s not the same for both types).

Scalability: Training multiple time series groups and automation is a huge need area in organizations. You need to know how to scale your analyses to thousands of time series.

Strengths and weaknesses: Did you know certain machine learning models are better for trend, seasonality, but not both? Why is ARIMA way better for certain datasets? When will Random Forest and XGBoost fail?

Advanced machine learning and deep learning: Recurrent Neural Networks (RRNs) have been crushing time series competitions. Will they work for business data? How can you implement them?
I teach each of these techniques and strategies so you become the time series expert for your organization. Here’s how. 👇
Advanced Time Series Course
Become the times series domain expert in your organization.
Make sure you’re notified when my new Advanced Time Series Forecasting in R course comes out. You’ll learn timetk
and modeltime
plus the most powerful time series forecasting techiniques available. Become the times series domain expert in your organization.
👉 Get notified here: Advanced Time Series Course.
You will learn:
 Time Series Preprocessing, Noise Reduction, & Anomaly Detection
 Feature engineering using lagged variables & external regressors
 Hyperparameter tuning
 Time series crossvalidation
 Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
 NEW  Deep Learning with RNNs (Competition Winner)
 and more.
Signup for the Time Series Course waitlist
Future Work
I’m just getting started with modeltime
. The main functionality should not change so you can begin using. Let me know of any issues via GitHub. Regarding future work, here’s a short list of what’s coming over the next few months.
Ensembles and Model Stacking
A top priority on the software roadmap is to include model ensembling, various techniques for combining models to improve forecast results. The plan is to collaborate with the tidymodels
team to develop ensembling tools.
More Time Series Algorithms
It’s critical to have a diverse set of algorithms included in modeltime
or as extensions to modeltime
because this improves the speed of experimentation, model selections, and moving into production. To support extensibility:
 I have Model Roadmap here for additional models.
 I also have a vignette with instructions to help developers extend
modeltime
, creating R packages that leverage the forecasting workflow.
Comment on GitHub Issue #5 to let me know what you would like to see or if you have plans to extend modeltime
.
Improvements
I have several improvements forthcoming. Probably the most important of which is the confidence interval calculations. I plan to use the method used by earth::earth()
, which calculates prediction intervals by regressing the absolute errors vs the predictions. This should provide better approximation of forecast confidence.
Modeltime Resources
 Modeltime Documentation  Learn about
modeltime
workflow and which models have been included  Modeltime GitHub Page  Give it a Star if you like it!
 Timetk Documentation  Data wrangling, visualization, and preprocessing for time series.
 Tidymodels.org  The
tidymodels
framework is a collection of packages for modeling and machine learning usingtidyverse
principles.
Have questions about modeltime?
Make a comment in the chat below. 👇
And, if you plan on using modeltime
for your business, it’s a nobrainer  Join my Time Series Course Waitlist (It’s coming, it’s really insane).
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