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.

Installation

CRAN version:

install.packages("modeltime.resample")

Development version (latest features):

remotes::install_github("business-science/modeltime.resample")

Why Modeltime Resample?

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.

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)

Meet the modeltime ecosystem

Learn a growing ecosystem of forecasting packages

Modeltime is part of a growing ecosystem of Modeltime forecasting packages.

Take the High-Performance Forecasting Course

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High-Performance Time Series Course

Time Series is Changing

Time series is changing. Businesses now need 10,000+ time series forecasts every day. This is what I call a High-Performance Time Series Forecasting System (HPTSF) - Accurate, Robust, and Scalable Forecasting.

High-Performance Forecasting Systems will save companies by improving accuracy and scalability. Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:

• Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
• Deep Learning with GluonTS (Competition Winners)
• Time Series Preprocessing, Noise Reduction, & Anomaly Detection
• Feature engineering using lagged variables & external regressors
• Hyperparameter Tuning
• Time series cross-validation
• Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
• Scalable Forecasting - Forecast 1000+ time series in parallel
• and more.

Become the Time Series Expert for your organization.