UniTime：一种面向跨域时间序列预测的语言赋能统一模型
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
AI总结：
本文提出UniTime统一模型，通过领域指令、Language-TS Transformer和掩码策略解决跨域时间序列预测中的数据特征差异、领域区分困难和收敛速度不平衡问题，在预测性能和零样本迁移上达到领先水平。
摘要：
多变量时间序列预测在当代网络技术中发挥着关键作用。与为特定时间序列应用领域创建专用模型的传统方法不同，本研究倡导一种超越领域边界的统一模型范式。然而，学习有效的跨域模型面临以下挑战。首先，不同领域在数据特征上表现出差异，例如变量数量不同，这对那些对这些因素施加不灵活约束的现有模型构成了障碍。其次，模型可能难以区分来自不同领域的数据，导致在我们的评估中性能欠佳。第三，时间序列领域收敛速度的差异也可能导致经验性能受损。为解决这些问题，我们提出了UniTime，用于有效的跨域时间序列学习。具体而言，UniTime能够灵活适应具有不同特征的数据。它还使用领域指令和Language-TS Transformer来提供识别信息并对齐两种模态。此外，UniTime采用掩码来缓解领域收敛速度不平衡的问题。我们的大量实验证明了UniTime在提升最先进的预测性能和零样本迁移能力方面的有效性。
英文摘要：
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific time series application domains, this research advocates for a unified model paradigm that transcends domain boundaries. However, learning an effective cross-domain model presents the following challenges. First, various domains exhibit disparities in data characteristics, e.g., the number of variables, posing hurdles for existing models that impose inflexible constraints on these factors. Second, the model may encounter difficulties in distinguishing data from various domains, leading to suboptimal performance in our assessments. Third, the diverse convergence rates of time series domains can also result in compromised empirical performance. To address these issues, we propose UniTime for effective cross-domain time series learning. Concretely, UniTime can flexibly adapt to data with varying characteristics. It also uses domain instructions and a Language-TS Transformer to offer identification information and align two modalities. In addition, UniTime employs masking to alleviate domain convergence speed imbalance issues. Our extensive experiments demonstrate the effectiveness of UniTime in advancing state-of-the-art forecasting performance and zero-shot transferability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
