granite tsfm

We have hosted the application granite tsfm in order to run this application in our online workstations with Wine or directly.

Quick description about granite tsfm:

granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. Issues and examples in the tracker illustrate common tasks such as slicing inference windows or using pipeline helpers that return pandas DataFrames, grounding the library in day-to-day time-series operations. The ecosystem around TSFM also includes a community cookbook of “recipes” that showcase capabilities and patterns. Overall, the repo is designed as a hands-on companion for teams adopting time-series foundation models in production-leaning settings.

Features:

• Notebooks and scripts for training, evaluation, and serving
• Pipeline helpers that operate on pandas-friendly data structures
• Guidance to hosted TSFM model weights and service components
• Examples addressing windowing, slicing, and inference workflows
• Compatibility across modern Python versions (3.10–3.12)
• Community cookbook with practical time-series “recipes”

Programming Language: Python.
Categories: