TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification

• URL: http://arxiv.org/abs/2406.04419v2
• Date: Mon, 17 Mar 2025 17:40:41 GMT
• Title: TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
• Authors: Md Atik Ahamed, Qiang Cheng,
• Abstract summary: We propose a novel multi-view approach to capture patterns with properties like shift equivariance.<n>Our method integrates diverse features, including spectral, temporal, local, and global features, to obtain rich, complementary contexts for TSC.<n>Our approach achieves average accuracy improvements of 4.01-6.45% and 7.93% respectively, over leading TSC models.
• Score: 13.110156202816112
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate time series classification (TSC) is critical for various applications in fields such as healthcare and finance. While various approaches for TSC have been explored, important properties of time series, such as shift equivariance and inversion invariance, are largely underexplored by existing works. To fill this gap, we propose a novel multi-view approach to capture patterns with properties like shift equivariance. Our method integrates diverse features, including spectral, temporal, local, and global features, to obtain rich, complementary contexts for TSC. We use continuous wavelet transform to capture time-frequency features that remain consistent even when the input is shifted in time. These features are fused with temporal convolutional or multilayer perceptron features to provide complex local and global contextual information. We utilize the Mamba state space model for efficient and scalable sequence modeling and capturing long-range dependencies in time series. Moreover, we introduce a new scanning scheme for Mamba, called tango scanning, to effectively model sequence relationships and leverage inversion invariance, thereby enhancing our model's generalization and robustness. Experiments on two sets of benchmark datasets (10+20 datasets) demonstrate our approach's effectiveness, achieving average accuracy improvements of 4.01-6.45\% and 7.93\% respectively, over leading TSC models such as TimesNet and TSLANet.

Related papers

• MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification [18.142252811096643]
  We propose a novel Multiscale Periodic Time Series Network (MPTSNet) MPTSNet integrates multiscale local patterns and global correlations to fully exploit the inherent information in time series. The experiments on UEA benchmark datasets demonstrate that the proposed MPTSNet outperforms 21 existing advanced baselines in the MTSC tasks.
  arXiv  Detail & Related papers  (2025-03-07T17:07:51Z)
• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts [103.725112190618]
  This paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
  arXiv  Detail & Related papers  (2024-10-14T13:01:11Z)
• DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting [3.420673126033772]
  We propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. Our proposed model, named DRFormer, is evaluated on various real-world datasets, and experimental results demonstrate its superiority compared to existing methods.
  arXiv  Detail & Related papers  (2024-08-05T07:26:47Z)
• Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers [55.475142494272724]
  Time series prediction is crucial for understanding and forecasting complex dynamics in various domains. We introduce GridTST, a model that combines the benefits of two approaches using innovative multi-directional attentions. The model consistently delivers state-of-the-art performance across various real-world datasets.
  arXiv  Detail & Related papers  (2024-05-22T16:41:21Z)
• TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning [5.244482076690776]
  We introduce a novel multiple-instance learning (MIL) framework for better localization of patterns of interest and modeling time dependencies within time series. TimeMIL formulates the temporal correlation and ordering within a time-aware MIL pooling, leveraging a tokenized transformer with a specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art methods, underscoring the effectiveness of the weakly supervised TimeMIL in MTSC.
  arXiv  Detail & Related papers  (2024-05-06T03:27:23Z)
• TSLANet: Rethinking Transformers for Time Series Representation Learning [19.795353886621715]
  Time series data is characterized by its intrinsic long and short-range dependencies. We introduce a novel Time Series Lightweight Network (TSLANet) as a universal convolutional model for diverse time series tasks. Our experiments demonstrate that TSLANet outperforms state-of-the-art models in various tasks spanning classification, forecasting, and anomaly detection.
  arXiv  Detail & Related papers  (2024-04-12T13:41:29Z)
• EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series [7.514010315664322]
  We propose a novel anomaly detection method, named EdgeConvFormer, which integrates stacked Time2vec embedding, dynamic graph CNN, and Transformer to extract global and local spatial-time information. Experiments demonstrate that EdgeConvFormer can learn the spatial-temporal modeling from multivariate time series data and achieve better anomaly detection performance than the state-of-the-art approaches on many real-world datasets of different scales.
  arXiv  Detail & Related papers  (2023-12-04T08:38:54Z)
• Graph-Aware Contrasting for Multivariate Time-Series Classification [50.84488941336865]
  Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques. We propose Graph-Aware Contrasting for spatial consistency across MTS data. Our proposed method achieves state-of-the-art performance on various MTS classification tasks.
  arXiv  Detail & Related papers  (2023-09-11T02:35:22Z)
• 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)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• 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)
• 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)
• Multimodal Meta-Learning for Time Series Regression [3.135152720206844]
  We will explore the idea of using meta-learning for quickly adapting model parameters to new short-history time series. We show empirically that our proposed meta-learning method learns TSR with few data fast and outperforms the baselines in 9 of 12 experiments.
  arXiv  Detail & Related papers  (2021-08-05T20:50:18Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.