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：时间序列分析在气象预报、异常现象探测和行动识别等广泛应用中具有极大重要性。本文件侧重于时间变异模型,这是广泛分析任务的共同关键问题。 以往的方法试图直接从1D时间序列直接完成这一任务,由于时间模式复杂,这是一个极具挑战性的1D时间序列。 根据对多周期时间序列的观察,我们将复杂的时间变异推向多个周期内和周期间变异。 为解决1D时间序列在代表性能力方面的局限性,我们将时间变异分析扩展至2D空间,将1D时间序列转变为基于多个时期的2D 10个数据集。 这种变迁可以将周期内和周期间变异分别嵌入2D时间序列的列和行,使2D时间序列的变异很容易被2D内圈内和周期间变异变模拟。 技术上,我们提议用TimesNetNetNetNet网络和Timelblock作为时间序列的任务基础分析。TimesBlocklock可以发现多周期的适应性和时间变异,并提取基于多个时期的系统周期内流数据序列,包括Slental-del-Dlock-stal- ladalstalstal- sal- sal- sal- sal- sal- sal-s saltraction sal-laveal laveal- slational- slational- sal- sal- saltractional- sal- sal- sal- sal- saltraction)。