# Timesfm 3.0 Pytorch

URL: https://interfaze.ai/models/googletimesfm-30-pytorch

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Timesfm 3.0 Pytorch by google, a time-series-forecasting model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | Timesfm 3.0 Pytorch | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Timesfm 3.0 Pytorch | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/google/timesfm-3.0-pytorch)

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

This repository contains the official PyTorch weights and configurations for **TimesFM 3.0**.

## License

This model is released under the **[TimesFM Non-Commercial License v1.0](https://huggingface.co/google/timesfm-3.0-pytorch/blob/main/LICENSE)**.

## Model Details

-   **Architecture**: Stacked Mixing Transformer with Variate Attention and CPM Iterative RevIN.
-   **Context Patch Length**: 32
-   **Forecast Horizon Patch Length**: 64
-   **Layers**: 20 transformer layers (model dim: 1280, heads: 16)
-   **Quantiles**: (median at index 4)

## Data

timesfm-3.0 is pretrained using

-   GiftEvalPretrain excluding the datasets that overlap with fev-bench
-   Wikipedia Pageviews, cutoff Nov 2023 (see paper for details).
-   Google Trends top queries, cutoff EoY 2022 (see paper for details).
-   Synthetic and augmented data.

## Citation

@article{das2023decoder, title={A decoder-only foundation model for time-series forecasting}, author={Das, Abhimanyu and Kong, Weihao and Sen, Rajat and Zhou, Yichen}, journal={arXiv preprint arXiv:2310.10688}, year={2023} }

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