Transformers in Time Series: A Survey#
2. Affiliation:#
The first author’s affiliation is DAMO Academy, Alibaba Group, Bellevue, USA.
3. Keywords:#
Transformers, time series modeling, long-range dependencies, interactions, forecasting, anomaly detection, classification.
4. Urls:#
arXiv:2202.07125v4 [cs.LG] 10 Feb 2023, Github: https://github.com/qingsongedu/time-series-transformers-review
5. Summary:#
(1): This paper aims to systematically review the development and performance of Transformer schemes for time series modeling, as Transformer has shown great modeling ability for long-range dependencies and interactions in sequential data and thus are appealing to time series modeling.
(2): The past methods related to deep learning for time series have been surveyed, but there is no comprehensive survey for Transformers in time series. The paper proposes a new taxonomy from perspectives of both network modifications and application domains for time series Transformers. The motivation is to provide practical guidelines on how to effectively use Transformers for time series modeling.
(3): The research methodology proposed in this paper is to summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis, and categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, the authors perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series.
(4): The methods in this paper are applied to various time series tasks, such as forecasting, anomaly detection, and classification, and the paper concludes by discussing possible future directions for time series Transformers. The performance of the methods is well supported by the experimental analysis, and the paper provides useful research guidance for the time series community.
6. Methods:#
(1): The article systematically reviews the development and performance of Transformer schemes for time series modeling, using a new taxonomy that categorizes time series Transformers based on common tasks including forecasting, anomaly detection, and classification. The purpose of this method is to provide practical guidelines on how to effectively use Transformers for time series modeling.
(2): The authors propose a research methodology that involves summarizing the adaptations and modifications made to Transformers to accommodate the challenges in time series analysis. They also perform empirical analyses such as robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series.
(3): The methodological approach in this article is to comprehensively survey the past methods related to deep learning for time series and compare them with Transformers in time series. The authors also apply the methods in this paper to various time series tasks, such as forecasting, anomaly detection, and classification, and discuss possible future directions for time series Transformers.
7. Conclusion:#
(1): The significance of this piece of work lies in providing a comprehensive survey of the development and performance of Transformer schemes for time series modeling. This paper proposes a new taxonomy for Transformers in time series and offers practical guidelines for effectively using them in various tasks, such as forecasting, anomaly detection, and classification. It also discusses possible future directions for time series Transformers.
(2): Innovation point: The paper provides a new taxonomy for Transformers in time series from the perspectives of network modifications and application domains, offering a practical guideline for effectively using Transformers in various tasks.
(3): Performance: The empirical analysis in this paper, including robust analysis, model size analysis, and seasonal-trend decomposition analysis, shows the effectiveness of Transformers in time series modeling. However, some limitations are identified, such as scalability issues for large datasets and the need for specific data preprocessing methods.
(4): Workload: The research methodology proposed in this paper involves summarizing the adaptations and modifications made to Transformers and categorizing them based on common tasks. The paper’s empirical analysis requires a significant workload, including data preprocessing and model training. However, the results provide valuable insights for the time series community.