# TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

**Authors:** Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long  
**arXiv:** [2210.02186](https://arxiv.org/abs/2210.02186)  
**Date:** 2022-10-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.02186v1/

## Key points

- The paper addresses the problem of temporal variation modeling in time series analysis.
- Time series analysis is important for weather forecasting, anomaly detection, and action recognition.
- The authors propose a novel approach that leverages multi-periodicity in time series data.
- They decompose complex temporal variations into intraperiod- and interperiod-variations.
- The 1D time series data is transformed into 2D tensors based on multiple periods.
- Intraperiod-variations are embedded in the columns and interperiod-variations in the rows of the 2D tensors.
- The proposed method, TimesNet with TimesBlock, serves as a task-general backbone for time series analysis.
- TimesBlock module adaptively discovers multi-periodicity and extracts complex temporal variations using a parameter-efficient inception block.
- Experimental results show that TimesNet achieves state-of-the-art performance across five mainstream time series analysis tasks.

## Summary

The paper titled "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" addresses the problem of temporal variation modeling in time series analysis. Time series analysis plays a crucial role in various applications such as weather forecasting, anomaly detection, and action recognition. However, directly analyzing the intricate temporal patterns in 1D time series data is challenging. To overcome this limitation, the authors propose a novel approach that leverages the observation of multi-periodicity in time series. They decompose the complex temporal variations into multiple intraperiod- and interperiod-variations. This decomposition allows them to represent the temporal variations in a more expressive manner. The authors 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 enables them to embed intraperiod- and interperiod-variations into the columns and rows of the 2D tensors, respectively. As a result, they can easily model these 2D-variations using 2D kernels. The proposed method, called TimesNet with TimesBlock, serves as a task-general backbone for time series analysis. The TimesBlock module adaptively discovers multi-periodicity and extracts complex temporal variations from transformed 2D tensors using a parameter-efficient inception block. The experimental results demonstrate that TimesNet achieves state-of-the-art performance across five mainstream time series analysis tasks: short- and long-term forecasting, imputation, classification, and anomaly detection. In summary, this paper introduces an innovative approach for temporal variation modeling in time series analysis by leveraging multi-periodicity and transforming data into 2D tensors. The proposed TimesNet with TimesBlock demonstrates superior performance compared to previous methods across various time series analysis tasks.