用大模型做时间序列分类，发现Inception编码器最有效
An Exploratory Study to Repurpose LLMs to a Unified Architecture for Time Series Classification
- 混合架构：专用时序编码器+冻结大模型骨干
- Inception编码器在多种任务上表现最佳，其他均未显著提升
- 适合对大模型时序应用感兴趣的开发者和研究者
时间序列分类（TSC）是机器学习中的核心问题，应用广泛。近年来，人们尝试将大语言模型（LLMs）用于TSC，利用其强大的推理与泛化能力。以往工作主要关注如何将时序数据映射到文本域，但时序编码器架构的选择仍缺乏探索。本文开展一项探索性研究，测试多种混合架构：将不同类型的时序编码器（包括Inception、卷积、残差、Transformer和多层感知机）与冻结的LLM骨干结合。结果表明，只有Inception架构在多数任务中持续带来性能提升。该研究揭示了编码器选择在混合式大模型架构中的关键影响，并指出基于Inception的模型是未来大模型驱动时序学习的一个有前景方向。
原文摘要 · Abstract (English)
Time series classification (TSC) is a core machine learning problem with broad applications. Recently there has been growing interest in repurposing large language models (LLMs) for TSC, motivated by their strong reasoning and generalization ability. Prior work has primarily focused on alignment strategies that explicitly map time series data into the textual domain; however, the choice of time series encoder architecture remains underexplored. In this work, we conduct an exploratory study of hybrid architectures that combine specialized time series encoders with a frozen LLM backbone. We evaluate a diverse set of encoder families, including Inception, convolutional, residual, transformer-based, and multilayer perceptron architectures, among which the Inception model is the only encoder architecture that consistently yields positive performance gains when integrated with an LLM backbone. Overall, this study highlights the impact of time series encoder choice in hybrid LLM architectures and points to Inception-based models as a promising direction for future LLM-driven time series learning.
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