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README.md

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 ---
 license: apache-2.0
+pipeline_tag: time-series-forecasting
+tags:
+  - time series
+  - forecasting
+  - pretrained models
+  - foundation models
+  - time series foundation models
+  - time-series
+  - tinytimemixer
+  - granite-ttm-r3
 ---
+
+# Granite-TTM-R3 Model Card
+
+![](./ttm_image.webp)
+
+Building on top of **TTM-R1** and **TTM-R2**, we introduce the next generation of TinyTimeMixer under the Granite time-series foundation model family — **Granite-TTM-R3**. This release incorporates several novel *tiny-neural architectural innovations* designed to push the limits of **accuracy in high-speed forecasting**, a critical requirement for real-world production deployments.
+
+---
+
+## Supported Tasks & Capabilities
+
+**Granite-TTM-R3** is a family of pretrained models supporting multiple real-world forecasting scenarios:
+
+- **Zero-shot forecasting** across unseen datasets
+- **Few-shot adaptation** effective with as few as ~1K samples
+- **Full fine-tuning** for domain-specific optimization
+- **Multivariate time-series forecasting**
+- **Exogenous / control variable integration**
+- **High-throughput batch inference** for production systems
+
+---
+
+## Accuracy & Speed
+
+**Granite-TTM-R3** is engineered to achieve a strong balance between **state-of-the-art accuracy** and **extreme inference efficiency**, making it well-suited for real-world, high-throughput deployments.
+
+### Accuracy on GIFT-Eval 
+
+- Maintains **top-tier performance** on the GIFT-Eval benchmarks. 
+
+**Fine-tuned FM**
+
+- MASE: **0.718**
+- CRPS: **514**
+
+
+**Pre-trained FM**
+
+- MASE: **0.727**
+- CRPS: **523**
+
+---
+
+## ⚡ Inference Throughput
+
+**Granite-TTM-R3** delivers orders-of-magnitude faster inference compared to existing popular SOTA models.
+
+### GPU Throughput
+
+- Typical SOTA models: **~20–500 samples/sec**
+- **Granite-TTM-R3**: **~7,500 samples/sec**
+
+### CPU Throughput
+
+- Typical SOTA models: **~1–20 samples/sec**
+- **Granite-TTM-R3**: **~180 samples/sec**
+
+**Granite-TTM-R3 achieves ~15× speedup** over many existing approaches, without compromising accuracy, setting a strong benchmark for fast and reliable time-series forecasting.
+
+---
+
+## Architecture Overview
+
+Granite-TTM adopts a mixture-of-experts paradigm composed of models with varying complexities — approximately **1M–35M parameters** — coupled with a lightweight routing mechanism that automatically selects or blends the most suitable expert based on input data characteristics.
+
+This enables adaptive model selection, improving both accuracy and efficiency across diverse time-series scenarios.
+
+The architecture is built on efficient mixer-based designs that avoid expensive self-attention. Instead, Granite-TTM-R3 leverages linear gating-based attention mechanisms to capture temporal dependencies with significantly lower computational overhead.
+
+This combination allows **Granite-TTM-R3** to deliver scalable, adaptive, and ultra-fast forecasting performance suitable for real-time and large-scale deployments.
+
+---
+
+## What’s New in Granite-TTM-R3
+
+- **Trend–Residual Decomposition**  
+  Separately models long-term trends and high-frequency residuals for improved structural learning.
+
+- **Three-Stage Pre-Training**  
+  Sequential training of trend, residual, and joint components for better stability and convergence.
+
+- **Student-Teacher Pretraining**  
+  Student-teacher based pretraining for stable learning in noisy datasets.
+
+- **Enhanced Data Augmentation**  
+  Structured perturbations improve robustness across domains.
+
+- **Improved Normalization Strategy**  
+  Stabilizes training across scale shifts and heterogeneous datasets.
+
+- **GLU Gating**  
+  Dynamic information flow control within mixer blocks.
+
+- **Multi-Resolution Temporal Layer**  
+  Captures dependencies across short-, medium-, and long-term horizons.
+
+- **FFT-Based Embeddings**  
+  Incorporates frequency-domain signals to model periodicity and seasonality.
+
+- **Register Tokens**  
+  Learnable global tokens to encode sequence-level semantics.
+
+- **Multi-Quantile Forecasting Head**  
+  Enables probabilistic forecasting with multiple quantiles.
+
+- **Refined Loss Weighting**  
+  Balances trend, residual, and quantile objectives for improved calibration.
+
+---
+
+## ⚡ Why Granite-TTM-R3
+
+**Granite-TTM-R3** is purpose-built for production-grade time-series systems where:
+
+- **Low latency** is critical for real-time inference
+- **High scale** is required for millions of forecasts
+- **Data is heterogeneous and evolving**
+- **Compute constraints exist**, including CPU-friendly deployment
+
+It provides a practical combination of compactness, speed, and accuracy, making it suitable for industrial deployment.
+
+---
+
+## Example Recipes and Notebooks
+
+To be released.
+
+---
+
+## Publication
+
+- **Granite-TTM-R3 Paper**: To be released.
+- **Base TTM Paper**: [Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/874a4d89f2d04b4bcf9a2c19545cf040-Abstract-Conference.html)
+
+---
+
+## Training Data
+
+- Select Datasets from [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) and [GIFT-Eval Train](https://huggingface.co/datasets/Salesforce/GiftEval), using non-leaking historical context.
+- Custom synthesized data based on [KernelSynth](https://github.com/amazon-science/chronos-forecasting/blob/main/scripts/kernel-synth.py).
+
+---
+
+## Model Card Authors
+
+Vijay Ekambaram, Arindam Jati, Haoxiang Qiu, Takayuki Katsuki, Tomoya Sakai, Priyanshul Govil, Pankaj Dayama
+
+---
+
+## Citation
+
+Please cite the following paper if you intend to use this model or its associated architectures and approaches in your work.
+
+```bibtex
+@inproceedings{ekambaram2024tinytimemixersttms,
+  title={Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series},
+  author={Vijay Ekambaram and Arindam Jati and Pankaj Dayama and Sumanta Mukherjee and Nam H. Nguyen and Wesley M. Gifford and Chandra Reddy and Jayant Kalagnanam},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS 2024)},
+  year={2024},
+}