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Model Performance and Stability Assessment Tools for Single Time Series, Panel Data, & Cross-Sectional Time Series Analysis

A modeltime extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

Benefits: What Modeltime Resample Does

Resampling time series is an important strategy to evaluate the stability of models over time. However, it’s a pain to do this because it requires multiple for-loops to generate the predictions for multiple models and potentially multiple time series groups. Modeltime Resample simplifies the iterative forecasting process taking the pain away.

Modeltime Resample makes it easy to:

  1. Iteratively generate predictions from time series cross-validation plans.
  2. Evaluate the resample predictions to compare many time series models across multiple time-series windows.

Here is an example from Resampling Panel Data, where we can see that Prophet Boost and XGBoost Models outperform Prophet with Regressors for the Walmart Time Series Panel Dataset using the 6-Slice Time Series Cross Validation plan shown above.

Model Accuracy for 6 Time Series Resamples

Model Accuracy for 6 Time Series Resamples

Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)

Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)


Install the CRAN version:

# Not on CRAN yet
# install.packages("modeltime.resample")

Or, install the development version:


Getting Started

  1. Getting Started with Modeltime: Learn the basics of forecasting with Modeltime.
  2. Resampling a Single Time Series: Learn the basics of time series resample evaluation.
  3. Resampling Panel Data: An advanced tutorial on resample evaluation with multiple time series groups (Panel Data)

Learning More

My Talk on High-Performance Time Series Forecasting


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