timesfm README

The project's README from github.com/google-research/timesfm. As one markdown file: README.md.

TimesFM

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 3.0

Archived Model Versions:

Update — August 2026

TimesFM 3.0 is out!

TimesFM 3.0 introduces native multivariate time-series forecasting, flexible covariate support (both past-only and past-and-future covariates), superior zero-shot generalist capabilities, and top performance across all three major time-series foundation model benchmarks.

Key Highlights:

License notice for pretrained weights

Important: The TimesFM source code in this repository is licensed under Apache-2.0, and model weights up to version 2.5 remain Apache-2.0. However, for the time being, TimesFM 3.0 pretrained weights are distributed under the separate timesfm-non-commercial-license-v1.0 license and are restricted to non-commercial, non-production use. Commercial or production use of the default pretrained weights is not permitted.

Update - July 2, 2026

Updated PyPI to timesfm=2.0.2. 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:

Since the Sept. 2025 launch, the following improvements have been completed for TimesFM 2.5:

  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 TimesFM with PyTorch
pip install timesfm[torch]

# Or, for MLX-native inference on Apple silicon (no PyTorch required)
pip install timesfm[mlx]

Local Install

  1. Clone the repository:

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

  2. Create a virtual environment and install with PyTorch:

    # Using uv
uv venv
source .venv/bin/activate

 # Install the package in editable mode with torch
uv pip install -e .[torch]

Code Examples: TimesFM 3.0

1. Univariate Forecasting (Variable Lengths)

Pass a batch of 1D NumPy arrays of different context lengths to forecast univariate time series:

import numpy as np
from timesfm3 import TimesFM3Evaluator, ModelConfig

# Initialize TimesFM 3.0
config = ModelConfig(
    checkpoint_path="google/timesfm-3.0-pytorch",
    per_core_batch_size=32,
    device="cuda"
)
forecaster = TimesFM3Evaluator(config)

# Two univariate series of different lengths (100 and 72 steps)
ts1 = np.linspace(0, 1, 100).astype(np.float32)
ts2 = np.sin(np.linspace(0, 24, 72)).astype(np.float32)

# Generate forecast (point predictions + 9 quantiles: 0.1 to 0.9)
outputs = list(forecaster.predict_batch([ts1, ts2], horizon=12, return_quantiles=True, use_symmetric_averaging=False))

print("Series 1 forecast shape:", outputs[0].forecast.shape)   # (12,)
print("Series 1 quantiles shape:", outputs[0].quantiles.shape) # (12, 9)

print("Series 2 forecast shape:", outputs[1].forecast.shape)   # (12,)
print("Series 2 quantiles shape:", outputs[1].quantiles.shape) # (12, 9)

Apple Silicon: MLX backend

An MLX-native backend runs TimesFM 3.0 on Apple silicon without PyTorch. It mirrors the PyTorch TimesFM3Forecaster interface (predict / predict_batch, univariate or multivariate, with past-only and past-future covariates) and is numerically matched to it on google/timesfm-3.0-pytorch. Median forecast / quantile max abs error, context 512: 9.5e-7 / 1.8e-6 at horizon 64, 2.3e-6 / 2.7e-6 at horizon 128 (longer horizons stitch multiple output patches, so they are worth checking on their own).

import numpy as np
from timesfm3.mlx import TimesFM3Forecaster

forecaster = TimesFM3Forecaster.from_pretrained("google/timesfm-3.0-pytorch")

# Univariate, long horizon (>= 128 spans several output patches).
context = np.sin(np.linspace(0, 40, 512)).astype(np.float32)
out = forecaster.predict(context, horizon=128, return_quantiles=True)
print(out.forecast.shape)    # (128,)      median forecast
print(out.quantiles.shape)   # (128, 9)    9 deciles

# Batch many series through one forward pass.
outs = list(forecaster.predict_batch([context] * 32, horizon=128))

Multivariate targets and covariates work the same way as on the PyTorch backend (matched to 1.7e-6 on the checkpoint):

context_len, horizon = 256, 32

# Two target variates: (num_variates, context_len).
target = np.stack([
    np.sin(np.linspace(0, 24, context_len)),
    np.sin(np.linspace(1, 26, context_len)),
]).astype(np.float32)

past_only = np.random.randn(1, context_len).astype(np.float32)          # (1, 256)
past_future = np.sin(                                                    # (1, 256 + 32)
    np.linspace(0, 30, context_len + horizon)
)[None, :].astype(np.float32)

out = forecaster.predict(
    target,
    horizon=horizon,
    past_only_covariates=past_only,
    past_future_covariates=past_future,
    return_quantiles=True,
)
print(out.forecast.shape)    # (2, 32)     one forecast per target variate
print(out.quantiles.shape)   # (2, 32, 9)

Benchmarks (330M model, Apple M4 Max, context 512, horizon 64, fp32 with mx.compile):

batch p50 latency throughput
1 11.1 ms 90 series/s
8 19.7 ms 406 series/s
32 48.1 ms 666 series/s

Contexts longer than global_context (15,360) are truncated to their most recent points before decode, matching the PyTorch backend. use_symmetric_averaging, use_znorm, and padding_mode ("none" / "edge") are all supported and numerically matched to the PyTorch backend, so the MLX forecaster is a drop-in for the univariate and covariate forecasting paths.

2. Multivariate Forecasting with Covariates

Pass a 2D array of shape (num_variates, context_length) along with optional past-only and past-and-future covariates:

import numpy as np
from timesfm3 import TimesFM3Evaluator, ModelConfig

# Initialize TimesFM 3.0
config = ModelConfig(
    checkpoint_path="google/timesfm-3.0-pytorch",
    per_core_batch_size=16,
    device="cuda"
)
forecaster = TimesFM3Evaluator(config)

context_len = 128
horizon = 24

# 3 target variates across past context: (3, 128)
target = np.random.randn(3, context_len).astype(np.float32)

# 1 past-only covariate channel across past context: (1, 128)
past_only_cov = np.random.randn(1, context_len).astype(np.float32)

# 2 past-and-future covariate channels across context + horizon: (2, 152)
past_future_cov = np.random.randn(2, context_len + horizon).astype(np.float32)

# Generate joint forecast across all 3 target variates
outputs = list(
    forecaster.predict_batch(
        contexts=[target],
        horizon=horizon,
        past_only_covariates=[past_only_cov],
        past_future_covariates=[past_future_cov],
        return_quantiles=True,
        use_symmetric_averaging=False,
    )
)

print("Multivariate forecast shape:", outputs[0].forecast.shape)   # (3, 24)
print("Multivariate quantiles shape:", outputs[0].quantiles.shape) # (3, 24, 9)

timesfm (google-research/timesfm)

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34.6K ·GitHub
Timesfm
What is this?TimesFM is a pretrained time-series foundation model from Google Research used for time-series forecasting, similar in spirit to a language model but for numerical sequences.
Is it a library or a hosted service?It is a Python library/pretrained model you install and run yourself; Google also offers it as a product inside BigQuery ML, Google Sheets, and Vertex Model Garden, but this repo is the open-source version.
How do I install and run it?Install via PyPI (pip install timesfm) or clone the repo, create a virtual environment, and install with PyTorch support.
Do I need PyTorch, or is there another backend?PyTorch is the main backend, but there is also an MLX-native backend for running TimesFM 3.0 on Apple Silicon without PyTorch, and a Flax version for faster inference.
Can I use TimesFM 3.0 pretrained weights in a commercial product?No — the 3.0 pretrained weights are under a separate non-commercial license restricted to non-commercial, non-production use, even though the code itself is Apache-2.0.
Does it need Apple Silicon, or does it run on any machine?It runs on standard Linux/macOS/Windows machines via PyTorch, with an additional optimized MLX backend specifically for Apple Silicon Macs.
What makes TimesFM 3.0 different from earlier versions?TimesFM 3.0 adds native multivariate forecasting with both past-only and past-and-future covariate support, and ranks #1 across major benchmarks like fev-bench, TIME, and GIFT-Eval.
Can I fine-tune TimesFM on my own data?Yes, there is a fine-tuning example using HuggingFace Transformers with PEFT (LoRA) available in the examples/finetuning directory.
How long a context and forecast horizon does it support?TimesFM 2.5 supports up to 16k context length (up from 2048 in 2.0) and continuous quantile forecasting up to a 1k horizon via an optional quantile head.
How fast is inference on Apple Silicon?On an Apple M4 Max with the MLX backend (330M model, context 512, horizon 64), it achieves around 90 series/s at batch size 1 and up to 666 series/s at batch size 32.
What are similar projects?timeseries-atlas, LTX-Video, petals, jeeves, Model-Optimizer, NeoHorse, Kronos, nanoGPT
Which topics is it in?forecasting, deep-neural-networks, time-series, foundation-models, pretrained-models, lora, google, fine-tuning, large-language-models, transformer, apple-silicon, inference
Creator
google-research
Size
4.5 MB
Stars
34.6K
Updated
CommitSep 29Releasev3.0.0Aug 28
Age
2y
Commits · 90 Days