aiqbench analysis
This study investigates structured sparsity in time-series foundation models by dynamically skipping Transformer blocks at inference time, achieving significant latency reductions on A100 hardware with minimal accuracy loss under moderate sparsity. While the findings reveal cross-architecture redundancy and input-dependent optimization opportunities, they also highlight limitations in mask transferability across datasets and a lack of robust dynamic routing for unseen domains. Ultimately, the work establishes theoretical bounds for efficient inference but does not yet yield a fully deployable dynamic method.
How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models
arXiv:2610.13208v1 Announce Type: new Abstract: Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test whether whole Transformer blocks can be removed at inference time in two pretrained models: Chronos-Bolt and TimesFM 2.5. Our bypasses avoid the original block computation and preserve the dense model exactly when all blocks are enabled. Masks are selected only on calibration windows and evaluated on 1,088 held-out windows from ETTh1, Traffic, Weather, and Exchange. At moderate sparsity, Chronos-Bolt with 7/11 candidate blocks and TimesFM with 16/20 blocks show little change in mean relative forecasting quality across datasets while reducing median A100 batch-1 latency by 11.2% and 13.4%. More aggressive TimesFM skipping accelerates inference further but substantially harms Traffic and Weather. Across both models, different equal-budget masks win on different windows, and a retrospective oracle significantly improves over the best static mask on every dataset. However, dataset-specific masks transfer inconsistently, and Chronos representations do not support reliable unseen-domain routing. These results establish cross-architecture structured redundancy and input-dependent opportunity, but not a deployable dynamic inference method.
Title:How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models
Abstract:Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test whether whole Transformer blocks can be removed at inference time in two pretrained models: Chronos-Bolt and TimesFM 2.5. Our bypasses avoid the original block computation and preserve the dense model exactly when all blocks are enabled. Masks are selected only on calibration windows and evaluated on 1,088 held-out windows from ETTh1, Traffic, Weather, and Exchange. At moderate sparsity, Chronos-Bolt with 7/11 candidate blocks and TimesFM with 16/20 blocks show little change in mean relative forecasting quality across datasets while reducing median A100 batch-1 latency by 11.2% and 13.4%. More aggressive TimesFM skipping accelerates inference further but substantially harms Traffic and Weather. Across both models, different equal-budget masks win on different windows, and a retrospective oracle significantly improves over the best static mask on every dataset. However, dataset-specific masks transfer inconsistently, and Chronos representations do not support reliable unseen-domain routing. These results establish cross-architecture structured redundancy and input-dependent opportunity, but not a deployable dynamic inference method.
Machine Learning (cs.LG)
arXiv:2610.13208 [cs.LG]
(or arXiv:2610.13208v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.13208 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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