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

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

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.

Added back the covariate support through XReg for TimesFM 2.5.

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

1. add support for an upcoming Flax version of the model (faster inference).
2. add back covariate support.
3. populate more docstrings, docs and notebook.

1. Clone the repository:

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

2. 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]

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

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.