Model release

TimesFM 2.5 200M

TimesFM 2.5 200M is an open-weight model release in the TimesFM family from Google Research1, version 2.5 (timesfm-2.5-200m), released Sep 15, 2025. Its weights can be downloaded by the public2 3 4 under the Apache License 2.01 2 4.

TimesFM 2.5 is a 200M-parameter time-series forecasting model from Google Research, released in September 2025. Compared with TimesFM 2.0 it has fewer parameters (200M, down from 500M), a context length of up to 16k points, an optional 30M quantile head for continuous quantile forecasts up to a 1k horizon, and no frequency indicator input.4 1

On AI on Hugging Face: google/timesfm-2.5-200m-flax

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Sep 15, 2025

Availability and license

Overall availability

Public

Downloadable from Hugging Face without an access gate. The card states the checkpoint is not an officially supported Google product.2 1

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

Apache License 2.0 (external site: huggingface.co)1 2 4
Applies to: Weights · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0
Apache License 2.0 (timesfm source code) (external site: raw.githubusercontent.com)5
Applies to: Code · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

weights: Unknown — no complete fact-level rights review
code: Unknown — no complete fact-level rights review
data: Unknown — no complete fact-level rights review
documentation: Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Model-disclosure tier

Computed from the checklist below using USASI rubric v0.2. An editorial category, not a certification.

Open-weight

The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.

How tiers are computed

Public materials checklist

Items for a model under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for TimesFM 2.5 200M
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	PyTorch and Flax checkpoints are published on Hugging Face without an access gate.2 3 4
Inference codeIs code for running the model published?	Public	The Apache-2.0 timesfm package provides inference code; the model card includes a PyTorch example.1 4 5
Training codeIs the code used to train the model published?	Unknown	A LoRA fine-tuning example (Transformers with PEFT) was added in April 2026; no pre-training code for version 2.5 was found.4
Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness.	Unknown	Not assessed.
Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable.	Unknown	Not assessed.
Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient.	Unknown	Not assessed.
Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above.	Partial	The model card lists GiftEvalPretrain, Wikimedia pageviews (cutoff November 2023), Google Trends top queries (cutoff end of 2022), and synthetic and augmented data. The synthetic data is not documented as released.1
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Unknown	The ICML 2024 paper describes the original TimesFM approach; the changes in 2.5 are summarized only as a list in the README.1 4
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Unknown	No evaluation results or evaluation code specific to 2.5 were found in the sources read.

What it is useful for

The repository describes zero-shot point and quantile forecasting; covariate support through XReg was added for 2.5 in October 2025.4

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The model card shows loading the checkpoint with the timesfm package (TimesFM_2p5_200M_torch) and compiling a forecast configuration with a maximum context and horizon before forecasting.1

Organization context

Google (Alphabet)

Provenance and derivatives

Trained by Google Research as the third open TimesFM checkpoint. The card lists training data from GiftEvalPretrain (Salesforce), Wikimedia pageviews, Google Trends, and synthetic data. No other base model is named.1

Other releases in the TimesFM family

TimesFM 3.0Model-disclosure tier (USASI rubric v0.2): Open-weight

TimesFM family overview

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.

Training code: unknown.
Training-data information: unknown.
Training-data access: unknown.
Complete training pipeline: unknown.
Training recipe: unknown.
Evaluation materials: unknown.

Have a primary source? How to report a correction.

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: Documented U.S. control

The model card lists Google Research as author. Google Research is part of Google; Alphabet Inc.'s fiscal 2025 Form 10-K lists its principal executive offices in Mountain View, California, and Exhibit 21.01 lists Google LLC as a Delaware subsidiary of Alphabet.1 6 7

Assessed Sep 29, 2026

Sources

1.
google/timesfm-2.5-200m-pytorch model card (external site: huggingface.co)
Google Research (Hugging Face) · Model card · accessed Sep 29, 2026
2.
Hugging Face model metadata for google/timesfm-2.5-200m-pytorch (external site: huggingface.co)
Hugging Face · Model card · accessed Sep 29, 2026
3.
Hugging Face model metadata for google/timesfm-2.5-200m-flax (external site: huggingface.co)
Hugging Face · Model card · accessed Sep 29, 2026
4.
google-research/timesfm README (external site: raw.githubusercontent.com)
Google Research · Repository · accessed Sep 29, 2026
5.
google-research/timesfm LICENSE (Apache License 2.0) (external site: raw.githubusercontent.com)
Google Research · License · accessed Sep 29, 2026
6.
Alphabet Inc. Form 10-K for the fiscal year ended December 31, 2025 (external site: sec.gov)
Alphabet Inc. (via U.S. Securities and Exchange Commission EDGAR) · Filing · accessed Sep 29, 2026
7.
Alphabet Inc. Form 10-K fiscal 2025, Exhibit 21.01 (subsidiaries) (external site: sec.gov)
Alphabet Inc. (via U.S. Securities and Exchange Commission EDGAR) · Filing · accessed Sep 29, 2026

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Cite this page

APA style

USASI. (2026, September 29). TimesFM 2.5 200M. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/timesfm-2-5-200m/

BibTeX

@misc{usasi_timesfm_2_5_200m,
  author = {{USASI}},
  title = {TimesFM 2.5 200M},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/timesfm-2-5-200m/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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