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Home/Categories/AI & Machine Learning/timesfm
icon of timesfm

timesfm

Open source alternative to Google BigQuery

  • 34.7K stars
  • Apache-2.0
  • Active this month
  • Last commit 22 days ago

Last verified Oct 21, 2026

Generate zero-shot time-series forecasts with Google's Apache-2.0 foundation model for demand, traffic, finance, and operations planning.

Visit websiteGitHub34.7K stars
image of timesfm
TL;DR

timesfm is a decoder-only foundation model for time-series forecasting, pre-trained on 100 billion time-points. It offers zero-shot performance on various benchmarks, making it suitable for industries like retail and finance. The model is available on HuggingFace and GitHub for easy access and integration.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs, the quick start and the FAQ
who it's for

Who timesfm is for#

Retail Analyst

Use TimesFM to forecast product demand and optimize inventory levels.

Skip if:

If you require highly specialized models for niche products.

Financial Analyst

Use TimesFM to predict stock trends and market movements.

Skip if:

If you need real-time predictions with high-frequency data.

the problem

The problem it solves#

Traditional time-series forecasting methods require extensive training and validation cycles, making it difficult for users to quickly adapt models to new datasets.

how timesfm solves it

How it solves it#

Zero-shot Forecasting

Provides accurate forecasts on unseen time-series data without additional training.

Large Pre-training Dataset

Trained on 100 billion time-points, enhancing generalization across domains.

Compact Model Size

Utilizes only 200M parameters while achieving competitive performance.

Flexible Context and Horizon

Adapts to varying input lengths and can forecast long-term outcomes.

Open Source Access

Available on HuggingFace and GitHub for community use.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • High AccuracyDemonstrates superior performance compared to traditional statistical methods.
  • Rapid DeploymentEnables quick integration into existing workflows without extensive retraining.

Trade-offs

  • -Limited CustomizationMay not perform as well on highly specialized datasets without fine-tuning.
  • -Smaller Model SizeWhile efficient, it may lack some capabilities of larger models in specific scenarios.
install · quick start

Quick start#

bash
Install TimesFM in a Python environment when you want to run the open model locally or on your own CPU or GPU host. The current upstream README recommends cloning the repository and installing package extras with uv; choose torch, flax, or xreg based on your inference or fine-tuning path.

```bash
git clone https://github.com/google-research/timesfm.git
cd timesfm
uv venv
source .venv/bin/activate
uv pip install -e .[torch]
uv pip install -e .[xreg]
```
tech stack · detected from GitHub

What it's built on#

Languages
Python
frequently asked

FAQ#

What is TimesFM?

TimesFM is a decoder-only foundation model for time-series forecasting developed by Google Research.

How can I access TimesFM?

You can access TimesFM on HuggingFace and GitHub.

What industries can benefit from TimesFM?

Industries such as retail, finance, healthcare, and manufacturing can benefit from its forecasting capabilities.

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