metadata

license: apache-2.0

TimesFM

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

Resources and Technical Documentation:

Paper: A decoder-only foundation model for time-series forecasting, to appear in ICML 2024.
Google Research blog
GitHub repo

Authors: Google Research

This is not an officially supported Google product.

Installation

This Hugging Face repo hosts TimesFm checkpoints. Please visit our GitHub repo to install the timesfm library for model inference.

We have two environment files. For GPU installation (assuming CUDA 12 has been setup), you can create a conda environment tfm_env from the base folder through:

conda env create --file=environment.yml

For a CPU setup please use,

conda env create --file=environment_cpu.yml

to create the environment instead.

Follow by

conda activate tfm_env
pip install -e .

to install the package.

Usage

Then the base class can be loaded as,

import timesfm

tfm = timesfm.TimesFm(
    context_len=<context>,
    horizon_len=<horizon>,
    input_patch_len=32,
    output_patch_len=128,
    num_layers=20,
    model_dims=1280,
    backend=<backend>,
    per_core_batch_size=<batch_size>,
    quantiles=<quantiles>,
)
tfm.load_from_checkpoint(
    <checkpoint_path>,
    checkpoint_type=checkpoints.CheckpointType.FLAX,
)

The context_len here can be set as the max context length of the model. You can provide shorter series to the tfm.forecast() function and the model will handle it. Currently the model handles a max context length of 512, which can be increased in later releases.
The horizon length can be set to anything. We recommend setting it to the largest horizon length you would need in the forecasting tasks for your application. We generally recommend horizon length <= context length but it is not a requirement in the function call.
We also provide an API to forecast from pandas dataframe. Please look at the documentation of the function tfm.forecast_on_df().

Benchmarks

Please look into the README files in the respective benchmark directories within experiments/ for instructions for running TimesFM on the respective benchmarks.