siriuz42
104 lines
2.9 KiB
Markdown
104 lines
2.9 KiB
Markdown
# TimesFM
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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
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model developed by Google Research for time-series forecasting.
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* Paper:
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[A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688),
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ICML 2024.
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* All checkpoints:
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[TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).
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* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/).
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* [TimesFM in BigQuery](https://cloud.google.com/bigquery/docs/timesfm-model):
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an official Google product.
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This open version is not an officially supported Google product.
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**Latest Model Version:** TimesFM 2.5
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**Archived Model Versions:**
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- 1.0 and 2.0: relevant code archived in the sub directory `v1`. You can `pip
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install timesfm==1.3.0` to install an older version of this package to load
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them.
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## Update - Sept. 15, 2025
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TimesFM 2.5 is out!
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Comparing to TimesFM 2.0, this new 2.5 model:
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- uses 200M parameters, down from 500M.
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- supports up to 16k context length, up from 2048.
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- supports continuous quantile forecast up to 1k horizon via an optional 30M
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quantile head.
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- gets rid of the `frequency` indicator.
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- has a couple of new forecasting flags.
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Along with the model upgrade we have also upgraded the inference API. This repo
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will be under construction over the next few weeks to
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1. add support for an upcoming Flax version of the model (faster inference).
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2. add back covariate support.
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3. populate more docstrings, docs and notebook.
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### Install
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**Step 1** (Prerequisite)
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Install your preferred `torch` / `jax` backend based on your OS and accelerators
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(CPU, GPU, TPU or Apple Silicon).
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- [Install PyTorch](https://pytorch.org/get-started/locally/).
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- [Install Jax](https://docs.jax.dev/en/latest/installation.html#installation)
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for Flax.
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**Step 2** (Install `timesfm`)
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You can now install locally.
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```shell
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git clone https://github.com/google-research/timesfm.git
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cd timesfm
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pip install -e ".[torch]"
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```
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```shell
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git clone https://github.com/google-research/timesfm.git
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cd timesfm
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pip install -e ".[flax]"
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```
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### Code Example
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```python
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import torch
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import numpy as np
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import timesfm
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torch.set_float32_matmul_precision("high")
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model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
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model.compile(
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timesfm.ForecastConfig(
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max_context=1024,
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max_horizon=256,
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normalize_inputs=True,
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use_continuous_quantile_head=True,
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force_flip_invariance=True,
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infer_is_positive=True,
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fix_quantile_crossing=True,
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)
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)
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point_forecast, quantile_forecast = model.forecast(
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horizon=12,
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inputs=[
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np.linspace(0, 1, 100),
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np.sin(np.linspace(0, 20, 67)),
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], # Two dummy inputs
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)
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point_forecast.shape # (2, 12)
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quantile_forecast.shape # (2, 12, 10): mean, then 10th to 90th quantiles.
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```
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