Google Releases TimesFM 3.0, a 0.3B-Parameter Time-Series Forecasting Model, in PyTorch

Google Publishes TimesFM 3.0 Weights

Google Research has released the official PyTorch weights and configuration files for TimesFM 3.0, the latest version of its time-series foundation model, on Hugging Face. The model is designed specifically for time-series forecasting rather than general text or multimodal tasks.

Architecture and Specifications

TimesFM 3.0 uses a Stacked Mixing Transformer architecture with Variate Attention and CPM Iterative RevIN. According to the model card, key specifications include:

• Parameters: 0.3B
• Layers: 20 transformer layers
• Model dimension: 1280
• Attention heads: 16
• Context patch length: 32
• Forecast horizon patch length: 64
• Output format: Quantile forecasts, with the median at index 4
• Tensor type: F32, distributed as safetensors

This architecture differs from the decoder-only design described in the original TimesFM paper (arXiv:2310.10688, published October 2023), suggesting a structural revision alongside the version bump.

Training Data

Google states that TimesFM 3.0 was pretrained on GiftEvalPretrain, with datasets that overlap with the fev-bench benchmark excluded to avoid contamination. Additional training data includes:

• Wikipedia Pageviews data, with a cutoff of November 2023
• Google Trends top queries data, cutoff end of year 2022
• Synthetic and augmented time-series data

Google has not disclosed the total size of the training corpus or the exact composition breakdown between these sources in the released model card.

Licensing and Availability

The model is released under the TimesFM Non-Commercial License v1.0, meaning it cannot be used in commercial products or services without separate licensing arrangements from Google. As of publication, no inference provider has deployed the model for hosted access — Hugging Face's model page shows it is not currently served through its Inference Providers marketplace, and pricing has not been disclosed since the model is not commercially licensed.

No standardized benchmark scores (such as MASE or CRPS on common forecasting benchmarks) were included in the released model card, so independent verification of forecasting accuracy against competing models like Amazon's Chronos or Nixtla's TimeGPT is not yet available from this source.

What This Means

TimesFM 3.0 extends Google's push into foundation models for structured, non-language data — a category that has received far less attention than LLMs but has real demand in finance, supply chain, energy, and IT operations forecasting. The non-commercial license limits immediate enterprise adoption, positioning this release primarily for researchers and evaluators rather than production deployment. The absence of published benchmark numbers means claims about forecasting quality remain unverified until independent testing against established time-series benchmarks like GIFT-Eval or fev-bench is conducted.