Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

• URL: http://arxiv.org/abs/2411.04554v1
• Date: Thu, 07 Nov 2024 09:24:26 GMT
• Title: Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
• Authors: Qiang Wu, Gechang Yao, Zhixi Feng, Shuyuan Yang,
• Abstract summary: Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. Our proposed Peri-midFormer demonstrates outstanding performance in five mainstream time series analysis tasks.
• Score: 18.576473875972717
• License:
• Abstract: Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. However, this has been quite challenging due to the discrete nature of data points in time series and the complexity of periodic variation. In terms of periodicity, taking weather and traffic data as an example, there are multi-periodic variations such as yearly, monthly, weekly, and daily, etc. In order to break through the limitations of the previous methods, we decouple the implied complex periodic variations into inclusion and overlap relationships among different level periodic components based on the observation of the multi-periodicity therein and its inclusion relationships. This explicitly represents the naturally occurring pyramid-like properties in time series, where the top level is the original time series and lower levels consist of periodic components with gradually shorter periods, which we call the periodic pyramid. To further extract complex temporal variations, we introduce self-attention mechanism into the periodic pyramid, capturing complex periodic relationships by computing attention between periodic components based on their inclusion, overlap, and adjacency relationships. Our proposed Peri-midFormer demonstrates outstanding performance in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.

Related papers

• Causal Discovery-Driven Change Point Detection in Time Series [32.424281626708336]
  Change point detection in time series seeks to identify times when the probability distribution of time series changes. In practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions.
  arXiv  Detail & Related papers  (2024-07-10T00:54:42Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
  We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-17T06:29:09Z)
• Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series [0.6749750044497732]
  We introduce kernel-based statistical tests of joint independence in multivariate time series. We show how the method robustly uncovers significant higher-order dependencies in synthetic examples. Our method can aid in uncovering high-order interactions in data.
  arXiv  Detail & Related papers  (2023-05-15T10:38:24Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [80.56913334060404]
  Time series analysis is of immense importance in applications, such as weather forecasting, anomaly detection, and action recognition. Previous methods attempt to accomplish this directly from the 1D time series. We ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations.
  arXiv  Detail & Related papers  (2022-10-05T12:19:51Z)
• STD: A Seasonal-Trend-Dispersion Decomposition of Time Series [0.0]
  We propose a seasonal-trend-dispersion decomposition (STD) to deal with heteroscedasticity in time series. We show how STD can be used for time series analysis and forecasting.
  arXiv  Detail & Related papers  (2022-04-21T20:32:20Z)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [68.86835407617778]
  Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2021-10-06T10:33:55Z)
• Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning [7.616400192843963]
  We develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We detect anomalous intervals using the Maximally Divergent Interval (MDI) algorithm.
  arXiv  Detail & Related papers  (2021-09-14T10:15:52Z)
• RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection [36.254037216142244]
  We propose a robust and general framework for multiple periodicity detection. Our algorithm applies maximal overlap discrete wavelet transform to transform the time series into multiple temporal-frequency scales. Experiments on synthetic and real-world datasets show that our algorithm outperforms other popular ones for both single and multiple periodicity detection.
  arXiv  Detail & Related papers  (2020-02-21T20:10:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.