Frequency-Domain Time Series Modeling

Latest papers

22 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

AliO: Output Alignment Matters in Long-Term Time Series Forecasing
Oct 8, 2026Kwangryeol Park, Jaeho Kim, Seulki LeeFrequency-Domain Time Series Modeling Long-Term Time Series Forecasting
Artificial Intelligence Graduate School Ulsan National Institute of Science & Technology (UNIST), South Korea · Artificial Intelligence Korea University, South Korea · Department of Computer Science and Engineering Ulsan National Institute of Science & Technology (UNIST), South Korea
FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis
Oct 5, 2026Yubo Wang, Hui He, Hezhe Qiao +2Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling
School of Computer Science and Technology, Beijing Institute of Technology, No.5 Zhongguancun South Street, Beijing, 100081, China · School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore
TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra
Sep 26, 2026Weiwei Ye, Renhe Jiang, Hangchen Liu +2Non-Stationary Time Series Forecasting Probabilistic Time Series Forecasting
The University of Tokyo
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Aug 20, 2026Alexander Marusov, Dmitry Anikin, Alexey ZaytsevVariational Autoencoders Frequency-Domain Time Series Modeling
Applied AI Institute Moscow, Russia
Frequency-Domain Multi-Modality Transportation Modeling
Jul 9, 2026Jiewen Deng, Hangchen Liu, Junchen Li +2Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling
Southern University of Science and Technology · The University of Tokyo
Spectral Retrieval-Augmented Time-Series Forecasting
Jun 17, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Frequency-Domain Time Series Modeling Non-Stationary Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation
Jun 3, 2026Hongfan Gao, Wangmeng Shen, Bin Yang +1Frequency-Domain Time Series Modeling Time Series Imputation
School of Data Science and Engineering East China Normal University
FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting
May 31, 2026Mirza Samad Ahmed Baig, Syeda Anshrah GillaniLong-Term Time Series Forecasting Frequency-Domain Feature Learning
Fandaqah, Al Khobar, Saudi Arabia · Hamdard University, Karachi, Pakistan
FRWKV+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting
May 15, 2026Qingyuan Yang, Dongyue Chen, Da Teng +3Multivariate Time Series Forecasting Long-Term Time Series Forecasting
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China · Foshan Graduate School of Innovation, Northeastern University, Foshan, 528311, Guangdong, China · Northeastern University, Shenyang, 110819, Liaoning, China
Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction
May 7, 2026Alessandro Pagani, Marco Cominelli, Liying Han +11Frequency-Domain Feature Learning Time Series Representation Learning
DII, University of Brescia, Italy. · DEIB, Politecnico di Milano, Italy. · ECE Department, University of California, Los Angeles, USA. +6
AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
Apr 25, 2026Xudong Jiang, Mingshan Loo, Hanchen Yang +5Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Tongji University Shanghai, China · Fudan University Shanghai, China
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Apr 12, 2026Shunyu Wu, Jiawei Huang, Weibin Feng +6Time Series Forecasting Time-Series Foundation Models
Sun Yat-sen University · Xiaomi Corporation · National University of Singapore
Channel-wise Retrieval for Multivariate Time Series Forecasting
Apr 7, 2026Junhyeok Kang, Jun Seo, Soyeon Park +4Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling
LG AI Research, Republic of Korea
Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
Mar 28, 2026Shijie ZhangFrequency-Domain Time Series Modeling Time Series Generation
School of Computer Science and Engineering, Northeastern University, Shenyang, China
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 Modeling Time Series Forecasting
1Macquarie University · 2Shanghai Jiao Tong University · 3Nanyang Technological University +1
FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting
Mar 10, 2026Boya Zhang, Shuaijie Yin, Huiwen Zhu +1Time Series Forecasting Frequency-Domain Time Series Modeling
Shanghai Jiao Tong University, Shanghai, China
Harmonic Dataset Distillation for Time Series Forecasting
Mar 4, 2026Seungha Hong, Sanghwan Jang, Wonbin Kweon +3Frequency-Domain Time Series Modeling Dataset Distillation
Pohang University of Science and Technology (POSTECH), Republic of Korea · University of Illinois Urban-Champaign (UIUC), United States
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
Mar 4, 2026Xiang AoFrequency-Domain Time Series Modeling Robust Time Series Forecasting
School of Software Engineering, Beijing Jiaotong University, Beijing, China
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Feb 19, 2026Xihao Piao, Zheng Chen, Lingwei Zhu +3Frequency-Domain Time Series Modeling Non-Stationary Time Series Forecasting
SANKEN Osaka University Osaka, Japan · School of Information Science and Technology Great Bay University Dongguan, China · Department of Computer Science Florida State University
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Feb 2, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
Dec 17, 2025Runze Li, Hanchen Wang, Wenjie Zhang +4Frequency-Domain Time Series Modeling Time Series Imputation
University of New South Wales, Australia · University of Technology Sydney, Australia · The Chinese University of Hong Kong, Shenzhen, China
BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
Jan 31, 2025Zhixuan Li, Naipeng Chen, Seonghwa Choi +2Long-Term Time Series Forecasting Frequency-Domain Time Series Modeling
College of Computing and Data Science, Nanyang Technological University, Singapore · Department of Electrical and Electronic Engineering, Yonsei University, Korea

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AliO: Output Alignment Matters in Long-Term Time Series Forecasing

Kwangryeol Park, Jaeho Kim, Seulki Lee

Artificial Intelligence Graduate School Ulsan National Institute of Science & Technology (UNIST), South Korea · Artificial Intelligence Korea University, South Korea · Department of Computer Science and Engineering Ulsan National Institute of Science & Technology (UNIST), South Korea

Frequency-Domain Time Series Modeling Long-Term Time Series Forecasting Time Series Forecasting

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Long-term Time Series Forecasting (LTSF) tasks, which leverage the current data sequence as input to predict the future sequence, have become increasingly crucial in real-world applications such as weather forecasting and planning of electricity consumption. However, state-of-the-art LTSF models often fail to achieve prediction output alignment for the same timestamps across lagged input sequences. Instead, these models exhibit low output alignment, resulting in fluctuation in prediction outputs for the same timestamps, undermining the model's reliability. To address this, we propose AliO (Align Outputs), a novel approach designed to improve the output alignment of LTSF models by reducing the discrepancies between prediction outputs for the same timestamps in both the time and frequency domains. To measure output alignment, we introduce a new metric, TAM (Time Alignment Metric), which quantifies the alignment between prediction outputs, whereas existing metrics such as MSE only capture the distance between prediction outputs and ground truths. Experimental results show that AliO effectively improves the output alignment, i.e., up to 58.2% in TAM, while maintaining or enhancing the forecasting performance (up to 27.5%). This improved output alignment increases the reliability of the LTSF models, making them more applicable in real-world scenarios.