Model Performance and Stability Assessment Tools for Single Time Series, Panel Data, & Cross-Sectional Time Series Analysis
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.
Development version (latest features):
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:
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.
Learn a growing ecosystem of forecasting packages
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
Become the forecasting expert for your organization
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).
I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:
Modeltime- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
Become the Time Series Expert for your organization.