Higher-order Cross-structural Embedding Model for Time Series Analysis

• URL: http://arxiv.org/abs/2410.22984v1
• Date: Wed, 30 Oct 2024 12:51:14 GMT
• Title: Higher-order Cross-structural Embedding Model for Time Series Analysis
• Authors: Guancen Lin, Cong Shen, Aijing Lin,
• Abstract summary: Time series analysis has gained significant attention due to its critical applications in diverse fields such as healthcare, finance, and sensor networks. Current approaches struggle to model higher-order interactions within time series, and focus on learning temporal or spatial dependencies separately. We propose Higher-order Cross-structural Embedding Model for Time Series (High-TS), a novel framework that jointly models both temporal and spatial perspectives.
• Score: 12.35149125898563
• License:
• Abstract: Time series analysis has gained significant attention due to its critical applications in diverse fields such as healthcare, finance, and sensor networks. The complexity and non-stationarity of time series make it challenging to capture the interaction patterns across different timestamps. Current approaches struggle to model higher-order interactions within time series, and focus on learning temporal or spatial dependencies separately, which limits performance in downstream tasks. To address these gaps, we propose Higher-order Cross-structural Embedding Model for Time Series (High-TS), a novel framework that jointly models both temporal and spatial perspectives by combining multiscale Transformer with Topological Deep Learning (TDL). Meanwhile, High-TS utilizes contrastive learning to integrate these two structures for generating robust and discriminative representations. Extensive experiments show that High-TS outperforms state-of-the-art methods in various time series tasks and demonstrate the importance of higher-order cross-structural information in improving model performance.

Related papers

• TiVaT: Joint-Axis Attention for Time Series Forecasting with Lead-Lag Dynamics [5.016178141636157]
  TiVaT (Time-Variable Transformer) is a novel architecture that integrates temporal and variable dependencies. TiVaT consistently delivers strong performance across diverse datasets. This positions TiVaT as a new benchmark in MTS forecasting, particularly in handling datasets characterized by intricate and challenging dependencies.
  arXiv  Detail & Related papers  (2024-10-02T13:24:24Z)
• Understanding Different Design Choices in Training Large Time Series Models [71.20102277299445]
  Training Large Time Series Models (LTSMs) on heterogeneous time series data poses unique challenges. We propose emphtime series prompt, a novel statistical prompting strategy tailored to time series data. We introduce textttLTSM-bundle, which bundles the best design choices we have identified.
  arXiv  Detail & Related papers  (2024-06-20T07:09:19Z)
• Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning [11.19088022423885]
  We propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL. Results on two real-world MoST datasets verify the superiority of our approach compared with the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-06T08:24:06Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting [59.11817101030137]
  This research advocates for a unified model paradigm that transcends domain boundaries. Learning an effective cross-domain model presents the following challenges. We propose UniTime for effective cross-domain time series learning.
  arXiv  Detail & Related papers  (2023-10-15T06:30:22Z)
• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting [24.834846119163885]
  We propose a novel framework, TEMPO, that can effectively learn time series representations. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains.
  arXiv  Detail & Related papers  (2023-10-08T00:02:25Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)
• FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [53.55504611255664]
  FormerTime is a hierarchical representation model for improving the classification capacity for the multivariate time series classification task. It exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism.
  arXiv  Detail & Related papers  (2023-02-20T07:46:14Z)
• DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series [5.339109578928972]
  Asynchronous Time Series is a time series where all the channels are observed asynchronously-independently. This paper proposes a novel framework, that is highly scalable and memory efficient, for the asynchronous time series classification task. We explore convolutional neural networks, which are well researched for the closely related problem-classification of regularly sampled and fully observed time series.
  arXiv  Detail & Related papers  (2022-08-24T08:47:36Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)

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.