Time Series Decomposition

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Overview

Time series decomposition studies how to separate observed sequences into meaningful parts—often trend, seasonal or periodic patterns, and residual variation—to make forecasting and analysis more interpretable. Methods range from moving averages, Fourier and wavelet transforms, and low-rank factorization to learned, input-adaptive decompositions that handle changing frequencies and multiple time scales. Forecasting models often give each component a specialized role, while also combining information across variables, temporal patches, or spatial entities; decomposition is also used to extract interpretable features in applied settings. A central challenge is choosing components and scales that fit changing, noisy data: fixed decompositions can miss evolving patterns, while flexible learned ones may add complexity or fail on highly stochastic signals.

Latest papers

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20 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

  1. AdaTrendLike: Adaptive Hierarchical Detrending and Trend Likelihood Scoring for Time Series Anomaly Detection

    Oct 26, 2026Van Kwan Zhi Koh, Songnan Lin, Rongmo Luo +2Time-Series Anomaly DetectionInterpretable Anomaly Detection

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798. · Xylem Water Solutions Singapore Pte Ltd, Singapore 609935

  2. A decomposition-integration framework coupling singular spectrum analysis with a volatility-adaptive LSTM for stock price forecasting

    Oct 17, 2026Xin-Rui Lu, Da-Qing ZhangStock Price ForecastingTime Series Forecasting

    School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, PR China

  3. DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting

    Oct 15, 2026Yaohua Zha, Hai Wu, Shu-Tao XiaTime Series ForecastingMultivariate Time Series Forecasting

    Pengcheng Laboratory · Tsinghua Shenzhen International Graduate School, Tsinghua University

  4. DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling

    Sep 27, 2026Yiqiu Liu, Siru Zhong, Zhiguang Wang +2Time Series DecompositionNon-Stationary Time Series

    The Hong Kong University of Science and Technology (Guangzhou), China · Abel AI Lab, USA · Squirrel Ai Learning, USA

  5. Disentangling Heterogeneous Traffic Dynamics for Multi-Step Traffic Forecasting via Adaptive Spectral Decomposition

    Sep 22, 2026Zijun Huang, Chenrui Fu, Wenhao Wang +3Time Series DecompositionSpatiotemporal Forecasting

    Beijing Normal-Hong Kong Baptist University · Dalian Maritime University · National Chung Hsing University

  6. DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting

    Aug 20, 2026Alexander Marusov, Dmitry Anikin, Alexey ZaytsevVariational AutoencodersFrequency-Domain Time Series Modeling

    Applied AI Institute Moscow, Russia

  7. End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting

    Aug 9, 2026De-Yan Lu, Xugang Lu, Yu Tsao +1Koopman Operator LearningTime Series Forecasting

    Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · National Institute of Information and Communications Technology, Kyoto, Japan · Research Center for Information Technology Innovation, Academic Sinica, Taipei, Taiwan

  8. GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets

    Jun 26, 2026Janak M. Patel, Anirudh Deodhar, Dagnachew BirruMultivariate Time Series ForecastingLong-Term Time Series Forecasting

    Phi Labs, Quantiphi Marlborough, MA 01752, USA

  9. FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks

    Jun 23, 2026Nicholas Majeske, Ariful AzadExplainable Time Series ForecastingTime Series Forecasting

    Department of Intelligent Systems Engineering, Indiana University Bloomington, USA · Department of Computer Science and Engineering, Texas A&M University, USA

  10. Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting

    Jun 16, 2026Bin Wang, Heming Yang, Jinfang ShengMultivariate Time Series ForecastingLong-Term Time Series Forecasting

    School of Computer Science and Engineering, Central South University, HuNan, China.

  11. ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting

    May 24, 2026Jinjin Chi, Lei Feng, Yiming Wang +4Zero-Shot Time Series ForecastingTime Series Forecasting

    College of Computer Science and Technology, Jilin University, Changchun, China · College of Computing and Data Science, Nanyang Technological University, Singapore

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

    May 15, 2026Hua Wang, Xianhao Jiao, Fan ZhangMultivariate Time Series ForecastingLong-Term Time Series Forecasting

    School of Computer and Artificial Intelligence, Ludong University, Yantai, Shandong 264025, China · School of Computer and Artificial Intelligence, Ludong University, Yantai, Shandong 264025, China. · School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong 264005, China +1

  13. NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting

    May 8, 2026Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-ThiemeMultivariate Time Series ForecastingTime Series Forecasting

    1ISMLL, University of Hildesheim, VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany.

  14. Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition

    May 8, 2026Takato HondaLow-Rank Matrix DecompositionTime Series Forecasting

    Mellon Inc.

  15. PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting

    May 1, 2026Yingbo Zhou, Yutong Ye, Zhiwei Ling +5Multivariate Time Series ForecastingTime Series Forecasting

    Fudan University · Beihang University · Zhejiang University +1

  16. Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis

    Apr 24, 2026Victoria Ribeiro Rodrigues, Paul W. Davenport, Nicholas J. NapoliTime Series Decomposition

    Human Informatics and Predictive Performance Optimization (HIPPO) Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611 USA · Department of Physiological Sciences, University of Florida, Gainesville, FL 32611 USA

  17. SPaRSe-TIME: Saliency-Projected Low-Rank Temporal Modeling for Efficient and Interpretable Time Series Prediction

    Apr 19, 2026K. A. ShahriarTime Series ForecastingTime Series Decomposition

    Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka-1205, Bangladesh

  18. DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting

    Apr 1, 2026Xiang Ao, Yinyu Tan, Mengru ChenLong-Term Time Series ForecastingMultiscale Time-Series Modeling

    School of Software Engineering, Beijing Jiaotong University, Beijing, China

  19. MLOW: Interpretable Low-Rank Frequency Magnitude Decomposition of Multiple Effects for Time Series Forecasting

    Mar 19, 2026Runze Yang, Longbing Cao, Xiaoming Wu +4Frequency-Domain Time Series ModelingTime Series Forecasting

    1Macquarie University · 2Shanghai Jiao Tong University · 3Nanyang Technological University +1

  20. DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting

    Aug 3, 2025Haonan Yang, Jianchao Tang, Zhuo Li +1Time Series ForecastingMulti-Scale Feature Fusion

    National University of Defense Technology, Changsha, Hunan, China