Newest papers

Turning TS2Vec into a Foundation Model for Time Series Classification

Vasilii Feofanov, Zhenghan Tai, Andrei Ermakov, Irina Rish · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28602 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

TS2Vec的目标是对时间序列分类的最强有力的自我监督方法之一,于2022年推出,它旨在在在每个数据集上重新训练:自我监督的预先训练随后进行线性检测。自那时以来,该领域已转向基层模型,这些模型可以重复使用一个编码器在数据集中。然而TS2Vec提供一个紧凑的汇总架构,它直接在时间样本上运作,并接受变量长度输入,而无需重新采样。我们询问这些属性是否可以保留,当编码器仅经过一次预先训练,并表明它们可以:TS2Vec基本上可以转化为一个编码器的基层模型。三个变化足够:在合成体体上预先训练,输入编码器提供原始

Machine translation (Meta M2M100); the English original follows.

Abstract

TS2Vec is one of the strongest self-supervised methods for time series classification, introduced in 2022. It is designed to be trained anew on every dataset: self-supervised pre-training followed by linear probing. Since then, the field has shifted towards foundation models that reuse one encoder across datasets. Yet TS2Vec offers a compact convolutional architecture that operates directly on temporal samples and accepts variable-length inputs without resampling. We ask whether these properties can be retained when the encoder is pre-trained only once, and show that they can: TS2Vec can essentially be turned into a foundation model. Three changes suffice: pre-train on a synthetic corpus, feed the encoder a raw amplitude view and an instance-normalized shape view of the input through two small stems, and distill one specialist encoder per view into a single encoder. The result, TS2Vec-FM, is a 1.6M-parameter dilated convolutional network that, kept frozen, reaches 83.3% on UCR-128 and 75.3% on UEA-27 under linear probing, within 0.3 pp of MantisV2, the current state of the art, and above TS2Vec trained separately on each target dataset. What we find most interesting is how different TS2Vec-FM is from other foundation models in design: near-parity is reached without attention, without a patch tokenizer, and without intermediate-layer extraction, using an "old-school" convolutional encoder and a two-layer MLP stem.

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Topics: Encoder, Computer science, Foundation (evidence), Artificial intelligence, Field (mathematics), Reuse

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