GitHub - google-research/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub

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TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

• Paper: A decoder-only foundation model for time-series forecasting, ICML 2024.
• All checkpoints: TimesFM Hugging Face Collection.
• Google Research blog.
• TimesFM in Google 1P Products:
• BigQuery ML: Enterprise level SQL queries for scalability and reliability.
• Google Sheets: For your daily spreadsheet.
• Vertex Model Garden: Dockerized endpoint for agentic calling.

This open version is not an officially supported Google product.

Latest Model Version: TimesFM 2.5

Archived Model Versions:

• 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load them.

Update - June 5, 2026

Updated PyPI to timesfm=2.0.0. See Install.

Update - Apr. 9, 2026

Added fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see timesfm-forecasting/examples/finetuning/. Also added unit tests (tests/) and incorporated several community fixes.

Shoutout to @kashif and @darkpowerxo.

Update - Mar. 19, 2026

Huge shoutout to @borealBytes for adding the support for AGENTS! TimesFM SKILL.md is out.

Update - Oct. 29, 2025

Added back the covariate support through XReg for TimesFM 2.5.

Update - Sept. 15, 2025

TimesFM 2.5 is out!

Comparing to TimesFM 2.0, this new 2.5 model:

• uses 200M parameters, down from 500M.
• supports up to 16k context length, up from 2048.
• supports continuous quantile forecast up to 1k horizon via an optional 30M quantile head.
• gets rid of the frequency indicator.
• has a couple of new forecasting flags.

Since the Sept. 2025 launch, the following improvements have been completed:

1. ✅ Flax version of the model for faster inference.
2. ✅ Covariate support via XReg (see Oct. 2025 update).
3. ✅ Documentation, examples, and agent skill (see timesfm-forecasting/).
4. ✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see timesfm-forecasting/examples/finetuning/).
5. ✅ Unit tests for core layers, configs, and utilities (see tests/).

Install

From PyPI

Install the package with torch

pip install timesfm[torch]

Or with Flax

pip install timesfm[flax]

And when XReg is needed

pip install timesfm[xreg]

Local Install

1. Clone the repository:

git clone https://github.com/google-research/timesfm.git cd timesfm

1. Create a virtual environment and install dependencies using uv:

Create a virtual environment

uv venv

Activate the environment

source .venv/bin/activate

Install the package in editable mode with torch

uv pip install -e .[torch]

Or with flax

uv pip install -e .[flax]

And when XReg is needed

uv pip install -e .[xreg]

1. [Optional] Install your preferred torch / jax backend based on your OS and accelerators (CPU, GPU, TPU or Apple Silicon).:

2. Install PyTorch.

3. Install Jax for Flax.

Code Example

import torch import numpy as np import timesfm

torch.set_float32_matmul_precision("high")

model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")

model.compile( timesfm.ForecastConfig( max_context=1024, max_horizon=256, normalize_inputs=True, use_continuous_quantile_head=True, force_flip_invariance=True, infer_is_positive=True, fix_quantile_crossing=True, ) ) point_forecast, quantile_forecast = model.forecast( horizon=12, inputs=[ np.linspace(0, 1, 100), np.sin(np.linspace(0, 20, 67)), ], # Two dummy inputs ) point_forecast.shape # (2, 12) quantile_forecast.shape # (2, 12, 10): mean, then 10th to 90th quantiles.