Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks. Previous methods attempt to accomplish this directly from the 1D time series, which is extremely challenging due to the intricate temporal patterns. Based on the observation of multi-periodicity in time series, we ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations. To tackle the limitations of 1D time series in representation capability, we extend the analysis of temporal variations into the 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods. This transformation can embed the intraperiod- and interperiod-variations into the columns and rows of the 2D tensors respectively, making the 2D-variations to be easily modeled by 2D kernels. Technically, we propose the TimesNet with TimesBlock as a task-general backbone for time series analysis. TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block. Our proposed TimesNet achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection. Code is available at this repository: https://github.com/thuml/TimesNet.

翻译：时间序列分析在天气预报、异常检测和行为识别等广泛应用中具有极其重要的意义。本文聚焦于时序变化建模，这是众多分析任务中的共同关键问题。以往方法试图直接从一维时间序列中完成这一建模，但由于时序模式的复杂性，这一过程极具挑战性。基于时间序列中存在的多周期特性，我们将复杂的时序变化分解为多个周期内变化和周期间变化。为克服一维时间序列在表征能力上的局限性，我们通过将一维时间序列基于多周期转换为一组二维张量，将时序变化分析扩展至二维空间。这种转换能够将周期内变化和周期间变化分别嵌入二维张量的列和行中，从而使二维核能够轻松处理这些二维变化。在技术上，我们提出了以TimesBlock为核心的TimesNet，作为时间序列分析的任务通用骨干网络。TimesBlock能够自适应地发现多周期特性，并通过参数高效的Inception模块从转换后的二维张量中提取复杂时序变化。所提出的TimesNet在五种主流时间序列分析任务（包括短期与长期预测、插补、分类和异常检测）中均实现了持续的最优性能。代码已开源：https://github.com/thuml/TimesNet。