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ATFNet: An Adaptive Time-Frequency Ensemble Network for Accurate Long-term Time Series Forecasting


핵심 개념
ATFNet is an innovative framework that combines a time domain module and a frequency domain module to concurrently capture local and global dependencies in time series data, with a novel Dominant Harmonic Series Energy Weighting mechanism to dynamically adjust the weights between the two modules based on the periodicity of the input time series.
초록
The content discusses the development of ATFNet, an advanced time series forecasting framework that effectively combines time domain and frequency domain representations to capture both local and global dependencies in time series data. Key highlights: Real-world time series often exhibit a mix of periodic and non-periodic patterns, posing challenges for existing forecasting methods. Time domain analysis is superior in representing local dependencies, while frequency domain analysis excels in capturing global dependencies. ATFNet integrates a time domain module and a frequency domain module to leverage the strengths of both representations. The Dominant Harmonic Series Energy Weighting mechanism dynamically adjusts the weights between the two modules based on the periodicity of the input time series. The frequency domain module employs an Extended DFT to address the challenge of discrete frequency misalignment, and a Complex-valued Spectrum Attention mechanism to capture intricate relationships between different frequency combinations. Extensive experiments on multiple real-world datasets demonstrate that ATFNet outperforms current state-of-the-art methods in long-term time series forecasting.
통계
The periodic part of the ETTh1 variable 'OT' exhibits a clear harmonic group in the frequency domain. The periodic part of the Electricity variable '3' also shows a dominant harmonic group, while the non-periodic part of the Electricity variable '125' has a more uniform energy distribution across the frequency spectrum.
인용구
"Periodic time series typically exhibit more global dependencies, where the values of the series at one point in time are influenced by the values at other points in time that are separated by a specific interval. Non-periodic time series, on the other hand, exhibit more local dependencies." "Combining the advantages of both domains is a promising approach to address the challenge of dealing with the mixing of distinct periodic properties in real-world time series."

핵심 통찰 요약

by Hengyu Ye,Ji... 게시일 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05192.pdf
ATFNet

더 깊은 질문

How can the proposed Dominant Harmonic Series Energy Weighting mechanism be extended to handle time series with more complex periodic patterns, such as those with multiple dominant frequencies

The Dominant Harmonic Series Energy Weighting mechanism proposed in ATFNet can be extended to handle time series with more complex periodic patterns by incorporating a more sophisticated approach to identifying and weighting multiple dominant frequencies. One way to achieve this is by implementing a hierarchical weighting system that assigns different weights to each dominant frequency based on their significance in the spectrum. This can involve a multi-step process where the algorithm first identifies all dominant frequencies and then calculates their individual contributions to the overall periodicity of the time series. By assigning varying weights to each dominant frequency, the mechanism can effectively capture the complexity of the periodic patterns present in the data. Additionally, incorporating adaptive weighting strategies that adjust the weights dynamically based on the changing characteristics of the time series can further enhance the mechanism's ability to handle diverse and intricate periodic patterns.

What are the potential limitations of the ATFNet approach, and how could it be further improved to handle non-periodic time series more effectively

While ATFNet has shown promising results in long-term time series forecasting, there are potential limitations that could be addressed to improve its effectiveness in handling non-periodic time series. One limitation is the reliance on the Dominant Harmonic Series Energy Weighting mechanism, which may not be optimized for non-periodic data with irregular patterns. To enhance ATFNet's performance with non-periodic time series, the model could benefit from incorporating additional modules or mechanisms specifically designed to capture local dependencies and transient behaviors. For example, introducing a specialized module that focuses on detecting and analyzing short-term fluctuations in the data could improve the model's ability to handle non-periodic patterns. Furthermore, integrating techniques from anomaly detection and change point identification into ATFNet could provide valuable insights into irregularities and shifts in the time series data, enhancing its overall robustness and adaptability to diverse patterns.

Given the success of ATFNet in long-term time series forecasting, how could the insights and techniques developed in this work be applied to other time series analysis tasks, such as anomaly detection or change point identification

The insights and techniques developed in ATFNet for long-term time series forecasting can be applied to other time series analysis tasks such as anomaly detection or change point identification by leveraging the model's ability to capture both local and global dependencies in the data. For anomaly detection, ATFNet can be adapted to identify deviations from normal patterns by training the model on historical data and detecting anomalies based on discrepancies in the forecasted values. By incorporating anomaly detection mechanisms into ATFNet, anomalies can be detected in real-time, enabling proactive decision-making and risk mitigation. Similarly, for change point identification, ATFNet's capability to capture complex relationships in the time series data can be utilized to detect shifts or structural changes in the data. By analyzing the forecasted values for sudden variations or trends, ATFNet can effectively identify change points and provide valuable insights into the underlying dynamics of the time series.
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