MODEL PROFILE

granite-timeseries-tspulse-r1

ibm-granite · granite-timeseries-tspulse-r1

**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.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsOpen weights
Profile updated2026/11/2 19:16:21
OVERVIEW

Model overview

**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.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。