Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition

• URL: http://arxiv.org/abs/2603.13702v1
• Date: Sat, 14 Mar 2026 02:18:17 GMT
• Title: Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
• Abstract summary: We propose a generic plugin xCPD that can adaptively model the channel-patch dependencies from the perspective of graph spectral decomposition.<n>xCPD can be seamlessly integrated on top of existing CI and CD forecasting models, consistently enhancing both accuracy and generalization across benchmarks.
• Score: 44.75749696286735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting performance by modeling each channel individually, but it often suffers from poor generalization and overlooks meaningful inter-channel interactions. Conversely, Channel-Dependent (CD) strategies aggregate all channels, which may introduce irrelevant information and lead to oversmoothing. Despite recent progress, few existing methods offer the flexibility to adaptively balance CI and CD strategies in response to varying channel dependencies. To address this, we propose a generic plugin xCPD, that can adaptively model the channel-patch dependencies from the perspective of graph spectral decomposition. Specifically, xCPD first projects multivariate signals into the frequency domain using a shared graph Fourier basis, and groups patches into low-, mid-, and high-frequency bands based on their spectral energy responses. xCPD then applies a channel-adaptive routing mechanism that dynamically adjusts the degree of inter-channel interaction for each patch, enabling selective activation of frequency-specific experts. This facilitates fine-grained input-aware modeling of smooth trends, local fluctuations, and abrupt transitions. xCPD can be seamlessly integrated on top of existing CI and CD forecasting models, consistently enhancing both accuracy and generalization across benchmarks. The code is available https://github.com/Clearloveyuan/xCPD.

Related papers

• CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting [21.579831447953243]
  Channel-dependent models learn cross-channel features but often overfit the channel ordering.<n>Channel-independent models treat each channel in isolation to increase flexibility, yet this neglects inter-channel dependencies and limits performance.<n>We propose textbfCPiRi, a framework that infers cross-channel structure from data rather than memorizing a fixed ordering.
  arXiv  Detail & Related papers  (2026-01-28T07:30:32Z)
• CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting [67.3477355449697]
  We propose a Channel-wise Perceptual Loss (CP Loss) for time-series data.<n>We learn a unique perceptual space for each channel that is adapted to its characteristics.<n>Loss is calculated within these perception spaces to optimize the model.
  arXiv  Detail & Related papers  (2026-01-25T15:31:37Z)
• Context Video Semantic Transmission with Variable Length and Rate Coding over MIMO Channels [49.624608869195065]
  We propose the context video semantic transmission (CVST) framework for wireless video transmission.<n>We learn a context-channel correlation map to explicitly formulate the relationships between feature groups and multiple input multiple output (MIMO) subchannels.<n>We demonstrate substantial performance gains over various standardized separated coding methods and recent wireless video semantic communication approaches.
  arXiv  Detail & Related papers  (2025-12-23T10:48:43Z)
• Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting [49.40321003932633]
  Adapformer is an advanced Transformer-based framework that merges the benefits of CI and CD methodologies through effective channel management.<n>Adapformer achieves superior performance over existing models, enhancing both predictive accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-11-18T16:24:05Z)
• Channel-Aware Low-Rank Adaptation in Time Series Forecasting [43.684035409535696]
  Two representative channel strategies are closely associated with model expressivity and robustness. We present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components.
  arXiv  Detail & Related papers  (2024-07-24T13:05:17Z)
• SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion [59.96233305733875]
  Time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations. This paper presents an efficient-based model, the Series-cOre Fused Time Series forecaster (SOFTS)
  arXiv  Detail & Related papers  (2024-04-22T14:06:35Z)
• From Similarity to Superiority: Channel Clustering for Time Series Forecasting [61.96777031937871]
  We develop a novel and adaptable Channel Clustering Module ( CCM) CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster information instead of individual channel identities. CCM can boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively.
  arXiv  Detail & Related papers  (2024-03-31T02:46:27Z)
• MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer [8.329947472853029]
  Channel Independence (CI) strategy treats all channels as a single channel, expanding the dataset. Mixed Channels strategy combines the data expansion advantages of the CI strategy with the ability to counteract inter-channel correlation forgetting. Model blends a specific number of channels, leveraging an attention mechanism to effectively capture inter-channel correlation information.
  arXiv  Detail & Related papers  (2024-03-14T09:43:07Z)

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.