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、动作识别等广泛应用中具有重要意义。本文聚焦于时间变化建模，这是众多分析任务共同的关键问题。以往方法试图直接从一维时间序列完成这一任务，但由于复杂的时间模式，这极具挑战性。基于时间序列中存在的多周期性观测，我们将复杂的时间变化分解为多个周期内变化与周期间变化。为解决一维时间序列表示能力的局限性，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，将时间变化分析拓展至二维空间。这种变换可将周期内变化与周期间变化分别嵌入二维张量的列与行，从而使得二维变化易于通过二维卷积核进行建模。技术上，我们提出以TimesBlock为核心的TimesNet作为时间序列分析的任务通用主干网络。TimesBlock能够自适应发现多周期性，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间变化。所提出的TimesNet在五个主流时间序列分析任务（包括短长期预测、缺失值填补、分类及异常检测）中均达到一致的最优性能。代码已开源在：https://github.com/thuml/TimesNet。