  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.
