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 BigQuery: an official Google product.

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 - 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.

Along with the model upgrade we have also upgraded the inference API. This repo will be under construction over the next few weeks to

• add support for an upcoming Flax version of the model (faster inference).

• add back covariate support.

• populate more docstrings, docs and notebook.

Install

• Clone the repository:

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

• 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]

Or XReg is needed

uv pip install -e .[xreg]

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

• Install PyTorch.

• Install Jax for Flax.

Code Example

import torch import numpy as np import timesfm

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.`