TimesBERT：一种用于时间序列理解的BERT风格基础模型
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
AI总结：
本文提出TimesBERT，一种受多粒度结构启发的BERT风格基础模型，通过掩码建模和功能标记预测预训练于2600亿时间点，在分类、插补、异常检测等下游任务中达到最先进性能。
摘要：
时间序列分析在多种场景中至关重要。除了预测之外，大量现实世界任务被归类为分类、插补和异常检测，这些任务凸显了本文所称的时间序列理解的不同能力。虽然GPT风格模型已被定位为时间序列预测的基础模型，但BERT风格架构（在自然语言理解方面取得了显著进展）尚未被完全开发用于时间序列理解，这可能归因于BERT基本元素的不良丢弃。在本文中，受多元时间序列与多句文档之间共享的多粒度结构启发，我们设计了TimesBERT来学习时间序列的通用表示，包括时间模式和变量中心特征。除了掩码建模的自然适应外，我们还提出了一个功能标记预测的并行任务，以体现关键的多粒度结构。我们的模型在跨多个领域的2600亿个时间点上进行了预训练。利用多粒度表示，TimesBERT在四个典型的下游理解任务中取得了最先进的性能，超越了特定任务模型和语言预训练骨干模型，使其成为时间序列理解的多功能基础模型。
英文摘要：
Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, underscoring different capabilities termed time series understanding in this paper. While GPT-style models have been positioned as foundation models for time series forecasting, the BERT-style architecture, which has made significant advances in natural language understanding, has not been fully unlocked for time series understanding, possibly attributed to the undesirable dropout of essential elements of BERT. In this paper, inspired by the shared multi-granularity structure between multivariate time series and multisentence documents, we design TimesBERT to learn generic representations of time series including temporal patterns and variate-centric characteristics. In addition to a natural adaptation of masked modeling, we propose a parallel task of functional token prediction to embody vital multi-granularity structures. Our model is pre-trained on 260 billion time points across diverse domains. Leveraging multi-granularity representations, TimesBERT achieves state-of-the-art performance across four typical downstream understanding tasks, outperforming task-specific models and language pre-trained backbones, positioning it as a versatile foundation model for time series understanding.
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。
