Modeltime Resample provide a convenient toolkit for efficiently evaluating multiple models across time, increasing our confidence in model selections.
modeltime_resample()
, which automates the iterative model
fitting and prediction procedure.plot_modeltime_resamples()
provides a quick way to review
model resample accuracy visually.modeltime_resample_accuracy()
provides a flexible way for
creating custom accuracy tables using customizable summary functions
(e.g. mean, median, sd, min, max).Resampling gives us a way to compare multiple models across time.
In this tutorial, we’ll get you up to speed by evaluating multiple models using resampling of a single time series.
Load the following R packages.
library(tidymodels)
library(modeltime)
library(modeltime.resample)
library(tidyverse)
library(timetk)
We’ll work with the m750
data set.
%>%
m750 plot_time_series(date, value, .interactive = FALSE)
We’ll use timetk::time_series_cv()
to generate 4
time-series resamples.
"2 years"
"5 years"
"2 years
4
<- time_series_cv(
resamples_tscv data = m750,
assess = "2 years",
initial = "5 years",
skip = "2 years",
slice_limit = 4
)
resamples_tscv
## # Time Series Cross Validation Plan
## # A tibble: 4 x 2
## splits id
## <list> <chr>
## 1 <split [60/24]> Slice1
## 2 <split [60/24]> Slice2
## 3 <split [60/24]> Slice3
## 4 <split [60/24]> Slice4
Next, visualize the resample strategy to make sure we’re happy with our choices.
# Begin with a Cross Validation Strategy
%>%
resamples_tscv tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .facet_ncol = 2, .interactive = FALSE)
Create models and add them to a Modeltime Table with Modeltime.
I’ve already created 3 models (ARIMA, Prophet, and GLMNET) and saved the
results as part of the modeltime
package
m750_models
.
m750_models
## # Modeltime Table
## # A tibble: 3 x 3
## .model_id .model .model_desc
## <int> <list> <chr>
## 1 1 <workflow> ARIMA(0,1,1)(0,1,1)[12]
## 2 2 <workflow> PROPHET
## 3 3 <workflow> GLMNET
Generate resample predictions using
modeltime_fit_resamples()
:
m750_models
(models) and
m750_training_resamples
.resample_results
contains the resample predictions<- m750_models %>%
resamples_fitted modeltime_fit_resamples(
resamples = resamples_tscv,
control = control_resamples(verbose = FALSE)
)
resamples_fitted
## # Modeltime Table
## # A tibble: 3 x 4
## .model_id .model .model_desc .resample_results
## <int> <list> <chr> <list>
## 1 1 <workflow> ARIMA(0,1,1)(0,1,1)[12] <rsmp[+]>
## 2 2 <workflow> PROPHET <rsmp[+]>
## 3 3 <workflow> GLMNET <rsmp[+]>
Visualize the model resample accuracy using
plot_modeltime_resamples()
. Some observations:
%>%
resamples_fitted plot_modeltime_resamples(
.point_size = 3,
.point_alpha = 0.8,
.interactive = FALSE
)
We can compare the overall modeling approaches by evaluating the
results with modeltime_resample_accuracy()
. The default is
to report the average summary_fns = mean
, but this can be
changed to any summary function or a list containing multiple summary
functions (e.g. summary_fns = list(mean = mean, sd = sd)
).
From the table below, ARIMA has a 6% lower RMSE, indicating it’s the
best choice for consistent performance on this dataset.
%>%
resamples_fitted modeltime_resample_accuracy(summary_fns = mean) %>%
table_modeltime_accuracy(.interactive = FALSE)
Accuracy Table | |||||||||
.model_id | .model_desc | .type | n | mae | mape | mase | smape | rmse | rsq |
---|---|---|---|---|---|---|---|---|---|
1 | ARIMA(0,1,1)(0,1,1)[12] | Resamples | 4 | 421.78 | 4.11 | 1.64 | 4.15 | 490.88 | 0.77 |
2 | PROPHET | Resamples | 4 | 443.09 | 4.34 | 1.77 | 4.41 | 520.80 | 0.71 |
3 | GLMNET | Resamples | 4 | 451.48 | 4.42 | 1.71 | 4.48 | 522.40 | 0.81 |
Resampling gives us a way to compare multiple models across time. In this example, we can see that the ARIMA model performs better than the Prophet and GLMNET models with a lower RMSE. This won’t always be the case (every time series is different).
This is a quick overview of Getting Started with Modeltime Resample. To learn how to tune, ensemble, and work with multiple groups of Time Series, take my High-Performance Time Series Course.