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:时间序列分析在广泛应用中具有巨大重要性,例如气象预报、异常检测和动作识别。本文关注于时间序列中的时间变化建模,这是广泛分析任务的共同关键问题。先前的方法试图直接从一维时间序列中实现这一点,由于复杂的时间模式,这是极其困难的。基于对时间序列中的多周期性的观察,我们将复杂的时间变化分解成多个周期内和周期间的变化。为了解决一维时间序列表示能力的限制,我们将时间变化分析扩展到二维空间中,通过将一维时间序列转换为基于多个周期的一组二维张量来实现。这种转换可以将周期内和周期间的变化分别嵌入到二维张量的列和行中,从而使二维变化可以通过二维卷积核轻松地建模。在技术方面,我们提出了基于时间序列的二维变化建模框架TimesNet,并使用TimesBlock作为通用任务的骨干网络。通过一个效率高的Inception模块,TimesBlock可以自适应地发现多周期性,并从转换后的二维张量中提取复杂的时间变化。我们的提出的TimesNet在五个主流时间序列分析任务中均取得了始终如一的最先进水平,包括短期和长期预测、填充、分类和异常检测。该研究的代码已开源于https://github.com/thuml/TimesNet。