KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2506.08939v1
• Date: Tue, 10 Jun 2025 16:03:33 GMT
• Title: KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting
• Authors: Hang Ye, Gaoxiang Duan, Haoran Zeng, Yangxin Zhu, Lingxue Meng, Xiaoying Zheng, Yongxin Zhu,
• Abstract summary: Traditional time series decomposition methods are single and rely on fixed rules.<n>We introduce KARMA, with an Adaptive Time Channel Decomposition module (ATCD) to dynamically extract trend and seasonal components.<n>It further integrates a Hybrid Frequency-Time Decomposition module (HFTD) to further decompose Series into frequency-domain and time-domain.
• Score: 2.9629194094115183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate long-term and efficient time series forecasting is a key requirement for a variety of practical applications, and there are complex interleaving time dynamics in time series data that require decomposition modeling. Traditional time series decomposition methods are single and rely on fixed rules, which are insufficient for mining the potential information of the series and adapting to the dynamic characteristics of complex series. On the other hand, the Transformer-based models for time series forecasting struggle to effectively model long sequences and intricate dynamic relationships due to their high computational complexity. To overcome these limitations, we introduce KARMA, with an Adaptive Time Channel Decomposition module (ATCD) to dynamically extract trend and seasonal components. It further integrates a Hybrid Frequency-Time Decomposition module (HFTD) to further decompose Series into frequency-domain and time-domain. These components are coupled with multi-scale Mamba-based KarmaBlock to efficiently process global and local information in a coordinated manner. Experiments on eight real-world datasets from diverse domains well demonstrated that KARMA significantly outperforms mainstream baseline methods in both predictive accuracy and computational efficiency. Code and full results are available at this repository: https://github.com/yedadasd/KARMA

Related papers

• FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction [35.500889115486025]
  Time series prediction, a crucial task across various domains, faces significant challenges due to the inherent complexities of time series data.<n>Recent advancements in State-Space Models, such as Mamba, offer a more efficient alternative for long-term modeling, but they cannot capture multi-scale periodicity and transient dynamics effectively.<n>This paper proposes a novel framework, FLDmamba, addressing these limitations.
  arXiv  Detail & Related papers  (2025-07-17T05:39:15Z)
• TimeCF: A TimeMixer-Based Model with adaptive Convolution and Sharpness-Aware Minimization Frequency Domain Loss for long-term time seris forecasting [5.032613143415414]
  We propose a deep learning model TimeCF for long-term time series forecasting based on the TimeMixer.<n>TimeCF decomposes the original time series into sequences of different scales.<n>Different scales are aggregated through a Feed-Forward Network.
  arXiv  Detail & Related papers  (2025-05-23T06:39:20Z)
• Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines [5.543238821368548]
  Time series often exhibit significant diversity in their temporal patterns across different time spans and domains.<n>Time Tracker achieves state-of-the-art performance in predicting accuracy, model generalization and adaptability.
  arXiv  Detail & Related papers  (2025-05-21T06:18:41Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• 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)<n>MPTSNet integrates multiscale local patterns and global correlations to fully exploit the inherent information in time series.<n>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)
• UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba [7.594115034632109]
  We propose UmambaTSF, a novel long-term time series forecasting framework. It integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-15T04:56:43Z)
• 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)
• TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification [13.110156202816112]
  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.
  arXiv  Detail & Related papers  (2024-06-06T18:05:10Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• 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)
• 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.