D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting

• URL: http://arxiv.org/abs/2403.17814v1
• Date: Tue, 26 Mar 2024 15:52:36 GMT
• Title: D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
• Authors: Xiaobing Yuan, Ling Chen,
• Abstract summary: We propose D-PAD, a deep-shallow multi-frequency patterns disentangling neural network for time series forecasting. D-PAD achieves the state-of-the-art performance, outperforming the best baseline by an average of 9.48% and 7.15% in MSE and MAE, respectively.
• Score: 7.447606231770597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In time series forecasting, effectively disentangling intricate temporal patterns is crucial. While recent works endeavor to combine decomposition techniques with deep learning, multiple frequencies may still be mixed in the decomposed components, e.g., trend and seasonal. Furthermore, frequency domain analysis methods, e.g., Fourier and wavelet transforms, have limitations in resolution in the time domain and adaptability. In this paper, we propose D-PAD, a deep-shallow multi-frequency patterns disentangling neural network for time series forecasting. Specifically, a multi-component decomposing (MCD) block is introduced to decompose the series into components with different frequency ranges, corresponding to the "shallow" aspect. A decomposition-reconstruction-decomposition (D-R-D) module is proposed to progressively extract the information of frequencies mixed in the components, corresponding to the "deep" aspect. After that, an interaction and fusion (IF) module is used to further analyze the components. Extensive experiments on seven real-world datasets demonstrate that D-PAD achieves the state-of-the-art performance, outperforming the best baseline by an average of 9.48% and 7.15% in MSE and MAE, respectively.

Related papers

• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation [55.800319453296886]
  Time series with missing or irregularly sampled data are a persistent challenge in machine learning.<n>We introduce a different Lombiable--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data.
  arXiv  Detail & Related papers  (2025-06-20T14:48:42Z)
• KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting [2.9629194094115183]
  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.
  arXiv  Detail & Related papers  (2025-06-10T16:03:33Z)
• 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. We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis. Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting [15.333859971089236]
  We propose a KAN-based Frequency Decomposition Learning architecture (TimeKAN) to address the complex forecasting challenges caused by multiple frequency mixtures. TimeKAN mainly consists of three components: Cascaded Frequency Decomposition (CFD) blocks, Multi-order KAN Representation Learning (M-KAN) blocks and Frequency Mixing blocks.
  arXiv  Detail & Related papers  (2025-02-10T03:51:26Z)
• Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification [25.27495694566081]
  We propose an auxiliary content-aware balanced decoder (CBD) to optimize the encoding quality in the spectrum space within masked modeling scheme. CBD iterates on a series of fundamental blocks, and thanks to two tailored units, each block could progressively refine the masked representation.
  arXiv  Detail & Related papers  (2024-12-17T14:12:20Z)
• MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting [18.815152183468673]
  Time series forecasting is crucial in many fields, yet current deep learning models struggle with noise, data sparsity, and capturing complex patterns. This paper presents MFF-FTNet, a novel framework addressing these challenges by combining contrastive learning with multi-scale feature extraction. Extensive experiments on five real-world datasets demonstrate that MFF-FTNet significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-26T12:41:42Z)
• DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain [20.55626048513748]
  DCDepth is a novel framework for the long-standing monocular depth estimation task. It estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth.
  arXiv  Detail & Related papers  (2024-10-19T05:10:07Z)
• 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)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Frequency-Adaptive Pan-Sharpening with Mixture of Experts [22.28680499480492]
  We propose a novel Frequency Adaptive Mixture of Experts (FAME) learning framework for pan-sharpening. Our method performs the best against other state-of-the-art ones and comprises a strong generalization ability for real-world scenes.
  arXiv  Detail & Related papers  (2024-01-04T08:58:25Z)
• MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction [6.980076213134384]
  MultiWave is a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals. We show that MultiWave consistently identifies critical features and their frequency components, thus providing valuable insights into the applications studied.
  arXiv  Detail & Related papers  (2023-06-16T20:07:15Z)
• A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series [0.0]
  Indian Summer Monsoon Rainfall (ISMR) is a very complex time series. Conventional one-time decomposition technique suffers from a leak of information from the future. Moving Front (MF) method is proposed to prevent data leakage.
  arXiv  Detail & Related papers  (2023-03-11T12:07:26Z)
• Diffusion Probabilistic Model Made Slim [128.2227518929644]
  We introduce a customized design for slim diffusion probabilistic models (DPM) for light-weight image synthesis. We achieve 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T16:27:28Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)
• Wavelet-Based Network For High Dynamic Range Imaging [64.66969585951207]
  Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. In this work, we propose a novel frequency-guided end-to-end deep neural network (FNet) to conduct HDR fusion in the frequency domain, and Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details.
  arXiv  Detail & Related papers  (2021-08-03T12:26:33Z)

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.