IBM Granite Time Series Cookbook

The "Recipes" in the Granite Time Series Cookbook showcase the capabilities of the IBM Granite Time Series models.

Recipes

1. Energy Demand Forecasting - Basic Inference
2. Energy Demand Forecasting - Preprocessing and Performance Evaluation
3. Energy Demand Forecasting - Few-shot Fine-tuning
4. Bike Sharing Forecasting - Zero-shot, Fine-tuning, and Performance Evaluation
5. Getting Started with Watson X AI SDK
6. Retail Forecasting using M5 Sales Data - Few-shot, Fine-tuning, Evaluation, and Visualization
7. BasicMotions Classification - Fine-tuning and Performance Evaluation
8. Bike Sharing missing data Imputation - Zero-Shot
9. Getting Started with Time-Series Search - Zero-Shot

Build Status

Contributing

For information about contributing to this repo, code of conduct guidelines, etc., see the community CONTRIBUTING and Code of Conduct guides. All commits require DCO-signoff and GPG or SSH signing. The GitHub recommended code security settings are enforced on this public repository (which include the signing requirement).

For more background, please see the community discussions.

Licenses

The Granite Time Series Cookbook's base license is CC BY 4.0.

Code in this repository, including in notebook cells, is licensed under Apache 2.0.

Any example datasets committed to this repository are licensed under CDLA Permissive 2.0.

IBM Public Repository Disclosure

All content in these repositories including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward.