无需微调即可通用处理时间序列分析任务的统一模型
Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
- 采用多尺度Transformer与点级分词，兼顾精度与效率
- 在5个任务上零微调表现媲美专用模型
- 适合需要快速部署的工业级时序分析场景
我们提出Zeus，一种统一的无微调时间序列基础模型（TSFM），可在无需任务特定微调的情况下，在多种分析任务中实现卓越性能。与以往研究主要关注零样本预测但需针对其他任务微调不同，Zeus通过解决多任务泛化的两个核心挑战，填补了这一空白。首先，为协调点级粒度与长序列可扩展性，Zeus引入具有点级分词和U型层次结构的多尺度Transformer，有效平衡细粒度保真度与计算效率。其次，为适应不同任务间的诱导偏见差异，Zeus提出多目标时间掩码（MOTM），在单一框架内支持异构任务（如外推、插值和全局抽象）。在五个代表性任务上的广泛实验表明，Zeus在无微调设置下始终达到有竞争力的结果，凸显其作为通用时间序列基础模型的潜力。
原文摘要 · Abstract (English)
We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. Unlike prior studies that primarily focus on zero-shot forecasting but require task-specific tuning for other tasks, Zeus bridges this gap by addressing two fundamental challenges in multi-task generalization. First, to reconcile point-level granularity with long-sequence scalability, Zeus incorporates a multi-scale Transformer featuring point-wise tokenization and a U-shaped hierarchy, effectively balancing fine-grained fidelity with computational efficiency. Second, to accommodate varying inductive biases across different tasks, Zeus introduces Multi-Objective Temporal Masking (MOTM), a unified strategy that supports heterogeneous tasks (e.g., extrapolation, interpolation, and global abstraction) within a single framework. Extensive experiments across five representative tasks demonstrate that Zeus consistently achieves competitive results in tuning-free settings, underscoring its potential as a general-purpose TSFM.
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