ttm r3 EU AI Act Compliance Profile

TTM-R3 Model Card

Building on top of TTM-R1 and TTM-R2, we introduce the next generation of TinyTimeMixer — 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

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

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 (GIFT-Eval)

• Maintains top-tier performance on the GIFT-Eval leaderboard.
• Fine-tuned FM:
  • MASE: 0.713 | CRPS: 0.512
  • (Lite) MASE: 0.721 | CRPS: 0.516
• Pre-trained FM:
  • MASE: 0.724 | CRPS: 0.520
  • (Lite) MASE: 0.734 | CRPS: 0.526

⚡ Inference Throughput

TTM-R3 delivers orders-of-magnitude faster inference compared to existing popular SOTA models.

🖥️ GPU Throughput

• Typical SOTA models: ~20–500 samples/sec
• TTM-R3: ~7,500 samples/sec
• TTM-R3 Lite: ~18,000 samples/sec

💻 CPU Throughput

• Typical SOTA models: ~1–20 samples/sec
• TTM-R3: ~180 samples/sec
• TTM-R3 Lite: ~800 samples/sec

👉 TTM-R3 achieves ~15–50× speedup over many existing approaches, without compromising accuracy, setting a new benchmark

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