knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width='100%', fig.align = "center", fig.width = 7, fig.height = 5, message = FALSE, warning = FALSE ) # CRAN OMP THREAD LIMIT Sys.setenv("OMP_THREAD_LIMIT" = 1)
tidymodels made easy! This short tutorial shows how you can use:
prophet_boost(), and more
...to perform classical time series analysis and machine learning in one framework! See "Model List" for the full list of
For those that prefer video tutorials, we have an 11-minute YouTube Video that walks you through the Modeltime Workflow.
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
Load libraries to complete this short tutorial.
library(xgboost) library(tidymodels) library(modeltime) library(tidyverse) library(lubridate) library(timetk) # This toggles plots from plotly (interactive) to ggplot (static) interactive <- FALSE
# Data m750 <- m4_monthly %>% filter(id == "M750")
We can visualize the dataset.
m750 %>% plot_time_series(date, value, .interactive = interactive)
Let's split the data into training and test sets using
# Split Data 80/20 splits <- initial_time_split(m750, prop = 0.9)
We can easily create dozens of forecasting models by combining
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
# Model 1: auto_arima ---- model_fit_arima_no_boost <- arima_reg() %>% set_engine(engine = "auto_arima") %>% fit(value ~ date, data = training(splits))
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
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))
Next, create an Error-Trend-Season (ETS) model using an Exponential Smoothing State Space model. This is accomplished with
# Model 3: ets ---- model_fit_ets <- exp_smoothing() %>% set_engine(engine = "ets") %>% fit(value ~ date, data = training(splits))
We'll create a
prophet model using
# Model 4: prophet ---- model_fit_prophet <- prophet_reg() %>% set_engine(engine = "prophet") %>% fit(value ~ date, data = training(splits))
We can model time series linear regression (TSLM) using the
linear_reg() algorithm from
parsnip. The following derivatives of date are used:
# Model 5: lm ---- model_fit_lm <- linear_reg() %>% set_engine("lm") %>% fit(value ~ as.numeric(date) + factor(month(date, label = TRUE), ordered = FALSE), data = training(splits))
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))
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
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
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
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
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 (
calibration_tbl %>% modeltime_accuracy() %>% table_modeltime_accuracy( .interactive = interactive )
From the accuracy metrics:
The final step is to refit the models to the full dataset using
modeltime_refit() and forecast them forward.
refit_tbl <- calibration_tbl %>% modeltime_refit(data = m750) refit_tbl %>% modeltime_forecast(h = "3 years", actual_data = m750) %>% plot_modeltime_forecast( .legend_max_width = 25, # For mobile screens .interactive = interactive )
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.
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