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TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis
arXiv – CS AI|Vijay Ekambaram, Subodh Kumar, Arindam Jati, Sumanta Mukherjee, Tomoya Sakai, Pankaj Dayama, Wesley M. Gifford, Jayant Kalagnanam|
🤖AI Summary
IBM researchers introduce TSPulse, an ultra-lightweight pre-trained AI model with only 1M parameters that achieves state-of-the-art performance in time-series analysis tasks. The model uses disentangled representations across temporal, spectral, and semantic views, delivering significant performance gains of 20-50% across multiple diagnostic tasks while being 10-100x smaller than competing models.
Key Takeaways
- →TSPulse achieves remarkable efficiency with only 1M parameters while outperforming models that are 10-100x larger across 75+ datasets.
- →The model introduces novel disentangled representations separating temporal, spectral, and semantic views for better zero-shot transfer capabilities.
- →Performance gains include +20% on anomaly detection, +25% on similarity search, +50% on imputation, and +5-16% on multivariate classification tasks.
- →TSPulse enables GPU-free deployment and efficient fine-tuning, making advanced time-series analysis more accessible.
- →The model and source code are publicly available through IBM's Granite timeseries collection on Hugging Face.
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#tspulse#time-series#pre-trained-models#ibm#machine-learning#anomaly-detection#zero-shot#lightweight-ai#disentangled-representations
Read Original →via arXiv – CS AI
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