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2 weeks ago
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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.
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.
-
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] -
[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.
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.
