TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis (2210.02186v3)

Abstract: 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.

• The paper presents a novel approach that transforms 1D time series into 2D tensors via FFT-based period identification.
• The method effectively captures both intra-period and inter-period variations using a parameter-efficient TimesBlock architecture.
• Results demonstrate state-of-the-art performance in forecasting, imputation, classification, and anomaly detection on benchmarks such as ETT, M4, and UEA.

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

The paper "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" introduces a novel approach to addressing challenges in time series analysis by leveraging the concept of multi-periodicity and extending temporal variation modeling into 2D space. This method provides a structured framework for capturing complex temporal variations that traditional 1D analysis struggles with.

Core Contributions

1. Multi-Periodicity Exploration: The research identifies multi-periodicity—a common characteristic in many real-world time series datasets, like daily or seasonal trends—and demonstrates how these overlapping periods complicate traditional 1D analysis.
2. 2D Tensor Transformation: By converting 1D time series into multiple 2D tensors via period identification using Fast Fourier Transform (FFT), the paper innovatively organizes temporal data to capture both intraperiod and interperiod variations.
3. TimesNet Architecture: The proposed TimesNet, built around the TimesBlock component, utilizes a parameter-efficient inception block. This enables effective learning from 2D representations through modular design, allowing for efficient processing of temporal 2D-variations.

Numerical Performance

The TimesNet exhibits superior performance across diverse time series tasks:

• Forecasting: Produces state-of-the-art results in both long-term and short-term settings, demonstrated in various datasets like ETT and M4.
• Imputation: Handles data missingness effectively, outperforming existing models in scenarios with up to 50% missing data.
• Classification: Achieves the highest average accuracy in benchmark tests from the UEA archive, surpassing the current leading models.
• Anomaly Detection: Excels in precision and recall across major benchmarks like SMD and SWaT.

Methodological Insights

The key innovation is the transformation of time series data into 2D space, which reveals locality patterns that enhance the modeling capability of temporal variations. By integrating inception-like blocks typically used in computer vision, TimesNet successfully captures multi-scale dependencies, yielding better representation learning.

Implications and Future Directions

From a practical perspective, TimesNet significantly advances the state-of-the-art in time series analysis, suggesting potential applications in areas such as industrial monitoring and finance. The theoretical implications highlight the benefit of merging traditional signal processing techniques (e.g., FFT) with modern deep learning architectures to overcome the limitations of 1D models.

Future research could explore the implementation of TimesNet in large-scale pre-training settings, potentially establishing it as a general-purpose backbone. Additionally, integrating other advanced 2D vision models could further elevate its predictive capabilities.

In summary, TimesNet's transformation of temporal data into a 2D space represents a substantial advancement in the field, offering a robust, versatile framework suitable for a range of time series analysis tasks.