Long-Term Time Series Forecasting

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TriTS: Time Series Forecasting from a Multimodal Perspective
Apr 17, 2026Xiang AoLong-Term Time Series Forecasting Cross-Modal Representation Learning
School of Software Engineering, Beijing Jiaotong University Beijing 100044, China
Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting
Apr 10, 2026Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Data Science Group University of Hildesheim Hildesheim, Germany · Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Hildesheim, Germany
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
Mar 6, 2026Xingsheng Chen, Xianpei Mu, Deyu Yi +6Multivariate Time Series Forecasting Long-Term Time Series Forecasting
School of Computing and Data Science, The University of Hong Kong, Hong Kong, China · School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China · Innovation Engineering College, Macau University of Science and Technology, Macau, China +1
SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
Feb 18, 2026Xu Zhang, Qitong Wang, Peng Wang +1Long-Term Time Series Forecasting Multi-Scale Feature Fusion
Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Harvard University, Cambridge, Massachusetts, United States
Temporal horizons in forecasting: a performance-learnability trade-off
Jun 4, 2025Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2Long-Term Time Series Forecasting Time Series Forecasting
Institute of Neuroinformatics, ETH Zürich and University of Zürich, Winterhurerstrasse 190, Zürich 8057, Switzerland · School of Computing, National Australian University, 108 North Rd, Acton ACT 2601, Australia · Unit of Land Change Science, Swiss Federal Research Institute for Forest, Snow and Landscape Zürcherstrasse 111, Birmensdorf 8903, Switzerland
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
May 23, 2025Hao Wang, Licheng Pan, Zhichao Chen +5Long-Term Time Series Forecasting Time Series Forecasting
Xiaohongshu Inc. · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · Gaoling School of Artificial Intelligence, Renmin University of China +4
Human Supervisor Workload Prediction: Lag Horizon Selection
May 21, 2025Mark-Robin Giolando, Julie A. AdamsMultivariate Time Series Forecasting Long-Term Time Series Forecasting
Collaborative Robotics and Intelligent Systems Institute at Oregon State University, Corvallis, OR, USA
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
Aug 2, 2024Zibo Liu, Zhe Jiang, Zelin Xu +5Long-Term Time Series Forecasting Spatiotemporal Forecasting
Department of Computer & Information Science & Engineering, University of Florida · Department of Computer Science, University of Kentucky

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TriTS: Time Series Forecasting from a Multimodal Perspective

Xiang Ao

School of Software Engineering, Beijing Jiaotong University Beijing 100044, China

Long-Term Time Series Forecasting Cross-Modal Representation Learning Non-Stationary Time Series Forecasting Multimodal Fusion Multiscale Time-Series Modeling

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Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Series Forecasting (LTSF) remains a significant challenge because real-world signals contain highly entangled temporal dynamics that are difficult to fully capture from a purely 1D perspective. To break this representation bottleneck, we propose TriTS, a novel cross-modal disentanglement framework that projects 1D time series into orthogonal time, frequency, and 2D-vision spaces.To seamlessly bridge the 1D-to-2D modality gap without the prohibitive

O(N^2)

computational overhead of Vision Transformers (ViTs), we introduce a Period-Aware Reshaping strategy and incorporate Visual Mamba (Vim). This approach efficiently models cross-period dependencies as global visual textures while maintaining linear computational complexity. Complementing this, we design a Multi-Resolution Wavelet Mixing (MR-WM) module for the frequency modality, which explicitly decouples non-stationary signals into trend and noise components to achieve fine-grained time-frequency localization. Finally, a streaming linear branch is retained in the time domain to anchor numerical stability. By dynamically fusing these three complementary representations, TriTS effectively adapts to diverse data contexts. Extensive experiments across multiple benchmark datasets demonstrate that TriTS achieves state-of-the-art (SOTA) performance, fundamentally outperforming existing vision-based forecasters by drastically reducing both parameter count and inference latency.

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