Getting Started with Modeltime

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Forecasting with tidymodels made easy! This short tutorial shows how you can use:

To perform classical time series analysis and machine learning in one framework! See "Model List" for the full list of modeltime models.

Modeltime in Minutes (YouTube)

Introduction to Modeltime

(Click to Watch on YouTube)

The Modeltime Workflow

Here's the general process and where the functions fit.

knitr::include_graphics("modeltime_workflow.jpg")

Just follow the modeltime workflow, which is detailed in 6 convenient steps:

  1. Collect data and split into training and test sets
  2. Create & Fit Multiple Models
  3. Add fitted models to a Model Table
  4. Calibrate the models to a testing set.
  5. Perform Testing Set Forecast & Accuracy Evaluation
  6. Refit the models to Full Dataset & Forecast Forward

Let's go through a guided tour to kick the tires on modeltime.

Time Series Forecasting Example

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

Step 1 - Collect data and split into training and test sets.

# 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 initial_time_split()

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

Step 2 - Create & Fit Multiple Models

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).

Model 1: Auto ARIMA (Modeltime)

First, we create a basic univariate ARIMA model using "Auto Arima" using arima_reg()

# Model 1: auto_arima ----
model_fit_arima_no_boost <- arima_reg() %>%
    set_engine(engine = "auto_arima") %>%
    fit(value ~ date, data = training(splits))

Model 2: Boosted Auto ARIMA (Modeltime)

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))

Model 3: Exponential Smoothing (Modeltime)

Next, create an Error-Trend-Season (ETS) model using an Exponential Smoothing State Space model. This is accomplished with exp_smoothing().

# Model 3: ets ----
model_fit_ets <- exp_smoothing() %>%
    set_engine(engine = "ets") %>%
    fit(value ~ date, data = training(splits))

Model 4: Prophet (Modeltime)

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))

Model 5: Linear Regression (Parsnip)

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))

Model 6: MARS (Workflow)

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.

Step 3 - Add fitted models to a Model Table.

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:

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

Step 4 - Calibrate the model to a testing set.

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

Step 5 - Testing Set Forecast & Accuracy Evaluation

There are 2 critical parts to an evaluation.

5A - Visualizing the Forecast Test

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:

5B - Accuracy Metrics

We can use modeltime_accuracy() to collect common accuracy metrics. The default reports the following metrics using yardstick functions:

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.

calibration_tbl %>%
    modeltime_accuracy() %>%
    table_modeltime_accuracy(
        .interactive = interactive
    )

From the accuracy metrics:

Step 6 - Refit to Full Dataset & Forecast Forward

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
    )

Refitting - What happened?

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:

Summary

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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modeltime documentation built on July 16, 2021, 9:08 a.m.