DEDUPEDWhat do you want to do?
3 use cases
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Forecast a series without training anything
Load the TimesFM 3.0 checkpoint and predict a horizon with quantiles straight from your history.
View prompt
Using google-research/timesfm, install `pip install timesfm[torch]`, forecast the next 12 steps of each series in <my CSV> with TimesFM3Evaluator and return_quantiles, and write the medians and deciles to a new CSV.
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Forecast several related series with drivers
Give TimesFM 3.0 multiple target variates plus past only and past and future covariates in one call.
View prompt
With google-research/timesfm, forecast <my three product series> for 24 steps using my known promotions as a past and future covariate, and report the median and the 10th and 90th percentiles.
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Run forecasts on a Mac with no GPU server
Use the MLX native backend to run TimesFM 3.0 locally on Apple silicon without PyTorch.
View prompt
Using google-research/timesfm, set up the MLX backend on my Mac with `pip install timesfm[mlx]` and forecast <my series> 128 steps ahead with quantiles, reporting how long it takes.
What people are building with Timesfm
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Built withHacker News
Shipped a browser forecasting app running TimesFM with no server
He exported TimesFM 2.5 to ONNX and runs it fully in the browser, so stocks, hourly weather, Wikipedia traffic and seasonal series like CO2 at Mauna Loa are forecast locally with an 80 percent band. You can also paste or draw your own series, and the code and weights are published.
By dingobabies on Hacker News
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Built withthe web
Built a Kubernetes autoscaler that provisions from a TimesFM forecast
Presage reads a Prometheus signal and uses TimesFM in-cluster, with no training and no data leaving the cluster, to forecast demand at the moment new pods become ready. In their own backtest on simulated game-server traffic it was short 0.2 percent of the time against 1.5 percent for a conventional autoscaler, and every scaler starts in shadow mode.
By presage (breezycourses) on the web
Real builds shared on X, Hacker News, Reddit, DEV and GitHub, summed up by Deduped from each post and its links; each card links to the original post. See what people are building with other repos
Quick start with Timesfm
- Check you have what it needs: Python with pip, and `pip install timesfm[torch]`, or `pip install timesfm[mlx]` on Apple silicon; A CUDA GPU for the PyTorch examples, which set device to cuda; Your series as NumPy arrays.
- Open google-research/timesfm on GitHub and read the README and license.
- Install it with
pip install timesfm[torch] - Create a ModelConfig with checkpoint_path set to google/timesfm-3.0-pytorch and build a TimesFM3Evaluator as in the README
- Call forecaster.predict_batch with your list of 1D NumPy arrays, a horizon and return_quantiles=True
- Read outputs[0].forecast and outputs[0].quantiles, which have shapes like (12,) and (12, 9)
These steps come from our top use case, Forecast a series without training anything, which also has a copy-paste agent prompt.
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Get them freeAbout this repo
- Who it is forData scientists and analysts who want forecasts without training a model per series.
- Why it is hot35k stars, picked up by one AI source we track
- Sourcegithub.com/google-research/timesfm ยท Back to Hot repos
Requirements
- Python with pip, and `pip install timesfm[torch]`, or `pip install timesfm[mlx]` on Apple silicon
- A CUDA GPU for the PyTorch examples, which set device to cuda
- Your series as NumPy arrays
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Questions about Timesfm
What is Timesfm?
TimesFM is Google Research's pretrained time series foundation model. Version 3.0 forecasts univariate and multivariate series zero shot, supports past only and past and future covariates, and returns point forecasts plus nine quantiles. It installs from PyPI with a PyTorch backend or an MLX backend for Apple silicon, and the repo includes a LoRA fine-tuning example. It is an open-source project on GitHub: https://github.com/google-research/timesfm.
How do I use Timesfm?
Install it with pip install timesfm[torch]; then create a ModelConfig with checkpoint_path set to google/timesfm-3.0-pytorch and build a TimesFM3Evaluator as in the README; call forecaster.predict_batch with your list of 1D NumPy arrays, a horizon and return_quantiles=True. The use cases on this page give the full steps and a copy-paste agent prompt for each job.
Who is Timesfm for?
Data scientists and analysts who want forecasts without training a model per series.
What can I do with Timesfm?
3 use cases so far, for example: Forecast a series without training anything; Forecast several related series with drivers; Run forecasts on a Mac with no GPU server. Each has steps and a copy-paste agent prompt.
What do I need to run Timesfm?
Python with pip, and `pip install timesfm[torch]`, or `pip install timesfm[mlx]` on Apple silicon; A CUDA GPU for the PyTorch examples, which set device to cuda; Your series as NumPy arrays.
What are alternatives to Timesfm?
Other hot AI repos in Data and analytics: Chronos Forecasting, Crawlee Python, DLT, Ultralytics, Darts.
Is Timesfm safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.