modeltime.resample

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.

Model Accuracy for 6 Time Series Resamples

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

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

The modeltime ecosystem is growing

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

• Modeltime (Machine
  Learning)

• Modeltime H2O
  (AutoML)

• Modeltime GluonTS (Deep
  Learning)

• Modeltime Ensemble (Blending
  Forecasts)

• Modeltime Resample
  (Backtesting)

• Timetk (Feature Engineering, Data Wrangling, Time Series
  Visualization)

Take the High-Performance Forecasting Course

Become the forecasting expert for your organization

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.

Take
the High-Performance Time Series Forecasting Course