PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2605.16449v1
• Date: Fri, 15 May 2026 03:11:52 GMT
• Title: PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
• Abstract summary: PESD-TSF is a physics-inspired structured decomposition framework for long-term time series forecasting.<n>It incorporates continuous-time priors to modulate signal amplitudes, preserving periodic structures across deep layers.<n>Experiments on benchmark datasets demonstrate that PESD-TSF consistently achieves state-of-the-art performance.
• Score: 6.1337977581640075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep forecasting models often suffer from attenuated periodic perception and entangled trend-noise representations as network depth increases. Moreover, the widely adopted channel-independent paradigm, while improving training stability, disrupts intrinsic dynamic coordination among variables, hindering the modeling of cross-variable consistency in multivariate time series. To address these issues, we propose PESD-TSF, a physics-inspired structured decomposition framework for long-term time series forecasting that jointly emphasizes interpretability and predictive accuracy. PESD-TSF introduces three key designs. First, a Multiplicative Periodic Gating mechanism incorporates continuous-time priors to dynamically modulate signal amplitudes, preserving periodic structures across deep layers. Second, a multi-scale structured encoder integrates detrended attention with hierarchical sampling to explicitly decouple long-term trends from high-frequency variations while retaining fine-grained temporal semantics. Third, to recover disrupted inter-variable dependencies, we propose Cross-Scale Collaborative Attention (CSCA) together with an RLC regularization scheme, which reconstructs global inter-variable topology in deep feature spaces and enforces physically consistent collaboration through orthogonality and consistency constraints. Extensive experiments on benchmark datasets from multiple domains demonstrate that PESD-TSF consistently achieves state-of-the-art performance, with particularly strong gains on multivariate forecasting tasks involving complex inter-variable coupling, highlighting its superior structural modeling capability and generalization.

Related papers

• DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting [6.800568640042171]
  Time series forecasting is critical across domains such as finance, meteorology, and energy.<n>We propose a Dynamic Semantic Compression (DySCo) framework to address these challenges.<n>DySCo serves as a universal plug-and-play module, significantly enhancing the ability of mainstream models to capture long-term correlations.
  arXiv  Detail & Related papers  (2026-04-01T12:40:23Z)
• TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting [67.91113180885601]
  TimeAPN explicitly models and predicts non-stationary factors from both the time and frequency domains.<n>TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons.<n>It outperforms state-of-the-art reversible normalization methods.
  arXiv  Detail & Related papers  (2026-03-18T07:21:24Z)
• Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change [52.46888249268445]
  Long-term environmental monitoring requires the ability to reconstruct and align 3D models across repeated site visits separated by months or years.<n>Existing approaches rely on post-hoc alignment of independently reconstructed sessions.<n>We propose enforcing cross-session correspondences directly within a joint SfM reconstruction.
  arXiv  Detail & Related papers  (2026-02-24T06:12:51Z)
• A Decomposition-based State Space Model for Multivariate Time-Series Forecasting [0.0]
  We propose an end-to-end decomposition framework using three parallel deep state space model branches to capture trend, seasonal, and residual components.<n>Across standard benchmarks, DecompSSM outperformed strong baselines, indicating the effectiveness of combining component-wise deep state space models and global context refinement.
  arXiv  Detail & Related papers  (2026-02-05T07:17:08Z)
• Improving Day-Ahead Grid Carbon Intensity Forecasting by Joint Modeling of Local-Temporal and Cross-Variable Dependencies Across Different Frequencies [3.1953619915392246]
  Accurate forecasting of the grid carbon intensity factor (CIF) is critical for enabling demand-side management and reducing emissions in modern electricity systems.<n>Despite advances in deep learning-based methods, it remains challenging to capture the fine-grained local-temporal dependencies.<n>We propose a novel model that integrates two parallel modules.
  arXiv  Detail & Related papers  (2026-01-10T11:20:55Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• MSTN: Fast and Efficient Multivariate Time Series Model [0.0]
  We introduce the Multi-scale Temporal Network (MSTN), a novel deep learning architecture founded on a hierarchical multi-scale and sequence modeling principle.<n>MSTN integrates a convolutional encoder that constructs a hierarchical feature pyramid for local patterns and a sequence modeling component for long-range temporal dependencies.<n>Extensive evaluations across time-series long-horizon forecasting, imputation, classification and generalizability study demonstrate that MSTN achieves competitive state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2025-11-25T18:09:42Z)
• 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)
• 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)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)

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.