granite-timeseries-tspulse-r1 EU AI Act Compliance Profile

Granite-TimeSeries-TSPulse-R1 Model Card

🚀 Update (Mar 2026): TSPulse has been accepted at ICLR 2026.

TSPulse models are ultra-compact pre-trained models for time-series data, featuring just 1M parameters and supporting GPU-free inference. Designed for versatility, TSPulse excels across a wide range of tasks including classification, anomaly detection (AD), imputation, and similarity search.

At the architecture level, TSPulse introduces a novel dual-space masked reconstruction strategy that learns jointly from both time and frequency domains —- capturing complementary patterns in a unified embedding space. This is coupled with a dual-embedding disentanglement mechanism that produces both fine-grained embeddings for detailed analysis and semantic embeddings for broader understanding. These semantic embeddings are inherently robust to variations in time, magnitude, and noise, making them ideal for time-series retrieval.

At the task level, TSPulse integrates several innovations:

• TSLens: A fine-tuning module for task-aware feature extraction.
• Multi-head triangulation: Fuses outputs from multiple prediction streams to enhance anomaly detection robustness.
• Hybrid masking: Reduces bias during pre-training, improving zero-shot imputation.

Highlights

TSPulse demonstrates significant improvements on several benchmarks:

• +5–16% on UEA classification benchmarks
• +20% on the TSB-AD benchmark for anomaly detection (rank #1 in both univariate and multivariate)
• +50% in zero-shot imputation
• +25% in semantic similarity search

Despite being 10–100× smaller than competing models, TSPulse sets a new standard in efficient time-series foundation modeling—combining strong out-of-the-box performance, minimal compute requirements, and rapid adaptability to downstream tasks.

Links

• Paper: [ArXiV](htt

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