google-research/timesfm
timesfm
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Usage guide
timesfm is an open-source project around all, search with 25,620 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating timesfm for Python AI workflows.
- Comparing a GitHub project with 25,620 stars and current repository activity.
Pros
- timesfm has visible GitHub traction with 25,620 stars.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
timesfm should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
timesfm architecture preview
timesfm's main path starts at the entry surface, runs through timesfm core runtime, combines LLM / model client, Runtime context, and returns Grounded answers / search results.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Runtime
timesfm core runtime
The core coordinates project logic, configuration, and AI-related execution in Python.
Python
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Featured video
Nodematic Tutorials
TimesFM Time Series Forecasting (Google AI, Jupyter, and GPUs)
6,996 views ยท 2024-05-31
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
timesfm depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/google-research/timesfm.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ uvAdoption guidance and sources
Practical use cases
TimesFM (Time Series Foundation Model) is a pretrained time-series fou
This is one of the documented reasons to evaluate timesfm before choosing a stack.
Search project comparison
Compare timesfm with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official timesfm setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented timesfm example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is timesfm?
timesfm is an open-source search project. TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
How do I install timesfm?
Start with the official README. The first detected setup step is: git clone https://github.com/google-research/timesfm.git.
Is timesfm beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can timesfm be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does timesfm need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt timesfm?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.