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Big Models, Small Gains: Frozen Forecasters as Classifiers on MONSTER
Rachet2006
time series classificationpretrainingmodel evaluation
2611.06988
Builder Relevance
3h ago60%
Abstract
This paper investigates the effectiveness of frozen forecasting models as feature extractors for time series classification compared to strong non-pretrained classifiers.
Reality Card
Core Claim
Pretrained representations improve classification accuracy over random-weight counterparts but do not consistently outperform strong non-pretrained baselines.
Method / Result
Pretraining improves classification accuracy by up to 23 percentage points on five of six datasets.
Limitations
Frozen representations do not reliably outperform the strongest non-pretrained baseline in any setting, raising concerns about their practical utility.
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