Multivariate Time Series Forecasting

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The field predicts several changing quantities together, such as electricity demand across regions, weather at multiple stations, or patients’ vital signs, where one variable may provide clues about another. Common approaches include linear and MLP models, recurrent networks, Transformers and state-space models, as well as graph, frequency-based, and decomposition methods; newer work also explores probabilistic generation, foundation models, and irregularly sampled data. Models are commonly compared on benchmarks such as ETT, Electricity, Traffic, Weather, and GIFT-Eval, with growing attention to long horizons, uncertainty, and transfer across datasets. A central tension is that cross-variable links and extra context can improve forecasts when informative, but noisy, shifting, or irrelevant relationships can hurt accuracy, so robustness and efficiency matter alongside average error.

Latest papers

307 papers52 papers in the last four weeks, up 58% on the four weeks before. 0.3% of all new papers.

IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting
Oct 29, 2026Anushka Katiyar, Cong Feng, Jing Wang +3Solar Power Forecasting Probabilistic Time Series Forecasting
National Laboratory of the Rockies · Latimer Controls
Multistep and Multivariate Time Series Explainability with GradientSHAP: A Wind Power Case Study
Oct 29, 2026Robbie Southam, Zhong Fan, Ke LiExplainable Time Series Forecasting Wind Power Forecasting
Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation
Oct 29, 2026Rohit Murali, Benaissa Dekhici, Tao Chen +2Time Series Data Augmentation Multivariate Time Series Forecasting
School of Chemistry and Chemical Engineering, University of Surrey, Guildford, GU2 7XH, UK · Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford, GU2 7XH, UK · Supergen Bioenergy Impact Hub, Energy and Bioproducts Research Institute, UK +1
Self-Supervised Latent Action-Conditioned World Modeling for Time Series
Oct 27, 2026Chia-Han Kuo, Chengshuai Zhao, Huan Liu +1Self-Supervised Time Series Representation Learning Action-Conditioned World Models
Arizona State University
Pneuma: A Large-Scale Real-World Time Series Benchmark for Commercial Aircraft Predictive Maintenance
Oct 27, 2026Jiafeng Lin, Yutian Zhang, Huan Zhang +5Predictive Maintenance Multivariate Time Series Forecasting
China Southern Airlines, Guangzhou, China · School of Transportation Science and Engineering, Beihang University, Beijing, China
RankFormer: Rethinking Time Series Transformers through Representation Capacity Analysis and Progressive Decoding
Oct 27, 2026Peiwang Tang, Cheng HE, Xijie Liang +1Multivariate Time Series Forecasting Time Series Forecasting
University of Science and Technology of China, Hefei, Anhui, China · State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China · Shanghai Black Wing Asset Management, Shanghai, China
SpecLST: Spectral-Enhanced Long-Short Term Spatio-Temporal Multimodal Modeling for Traffic Forecasting
Oct 26, 2026Kenan Kang, Matthew M. Y. Kuo, Weihua LiSpatiotemporal Forecasting Traffic Forecasting
Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand · Department of Data Science and Artificial Intelligence, Auckland University of Technology, Auckland 1010, New Zealand
STeP-TS: Shared Temporal Filters with Pruning for Time Series
Oct 26, 2026Hanne Raum, Jasper Hoffmann, Joschka BoedeckerMultivariate Time Series Forecasting Multiscale Time-Series Modeling
Department of Computer Science Albert-Ludwigs-Universität Freiburg · Department of Computer Science and BrainLinks-BrainTools Albert-Ludwigs-Universität Freiburg
TimeWorld: Towards Action-Conditioned Multimodal World Models for Time Series Forecasting
Oct 26, 2026Hang Ni, Weijia Zhang, Hao LiuTime Series Forecasting Action-Conditioned World Models
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Learning What to Share: Expert Specialization for Time-Series Forecasting
Oct 24, 2026Bashar Khoury, Shadi Omari, Assaf SchusterMultivariate Time Series Forecasting Mixture-of-Experts Expert Specialization
Department of Computer Science, Technion – Israel Institute of Technology
The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting
Oct 24, 2026Shuhao Li, Weidong Yang, Yue Cui +2Multivariate Time Series Forecasting Multiscale Time-Series Modeling
Fudan University, Shanghai, China · Nanyang Technological University, Singapore · Zhuhai Fudan Innovation Research Institute, Zhuhai, China +3
Model-agnostic predictability quantification of multivariable time series
Oct 23, 2026Yuxuan Shu, Vasileios LamposMultivariate Time Series Forecasting Time Series Forecasting with Covariates
Centre for Artificial Intelligence, Department of Computer Science, University College London
DBCM: Dual-Branch Calibration Module for Test-Time Adaptation in Time Series Forecasting
Oct 23, 2026Jaebyeong Jeon, Kunwoo Kang, Sang Yup Lee +1Time Series Forecasting Continual Test-Time Adaptation
Department of Statistics and Data Science, Yonsei University · Digital Analytics, College of Computing, Yonsei University · Department of Communication, Yonsei University
Local and Global Recursive Ridge Regression for Online Residual Correction of Frozen Multivariate Forecasters under Label Delay
Oct 21, 2026Takumi Fujimoto, Hiroaki NishiMultivariate Time Series Forecasting Online Time Series Forecasting
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Japan
Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks
Oct 20, 2026Erik Stolt, Sanja Karilanova, Ayca OzcelikkaleTime Series Forecasting Spiking Neural Networks
Department of Electrical Engineering, Uppsala University, Uppsala, Sweden
Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution
Oct 20, 2026Laurent Mombaerts, Jonathan Taws, Jacopo Gargano +4LLM-Based Time Series Forecasting Prompt Optimization
Amazon Web Services · University of California
CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries
Oct 19, 2026Shusen Ma, Xudong Zuo, Yangchun Xie +1Multivariate Time Series Forecasting Time Series Modeling
Institute of Advanced Technology, University of Science and Technology of China, Hefei, China · Department of Automation, University of Science and Technology of China, Hefei, China
OceanTensorCube and ST-MMT: A Multimodal Data and Transformer System for Seven-Day Forecasting of Pyropia Bleaching
Oct 19, 2026Gwangjin Kim, Cheolyoung Jang, Yosep JoTime Series Forecasting Spatiotemporal Forecasting
NCS Instructor, Smart Human Resources Development Institute, Republic of Korea · NH Networks LLC, Republic of Korea · DreamTech Co., Ltd., Republic of Korea
ActTime: Action-Conditioned World Modeling for Time Series
Oct 19, 2026Xinxing Zhou, Xiaojie Yuan, Yanlong Wen +1Action-Conditioned World Models Model-Based Planning
Nankai University · Griffith University
CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction
Oct 18, 2026Ankitkumar Joshi, Milos HauskrechtMultivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260
Compose the Horizon: Parameter-Shared Blockwise Forecasting under a Sequential-Compute Trade-off
Oct 17, 2026Zeyan Li, Weiheng Li, Shengda Zhuo +3Long-Term Time Series Forecasting Time Series Forecasting
Shanghai Jiao Tong University Shanghai, China · Jinan University Guangzhou, China · The University of Chicago Chicago, IL, USA +1
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Oct 16, 2026Aakriti, Manoj Cherukumalli, Prathamesh Patil +5Time Series Forecasting Probabilistic Time Series Forecasting
PathFM: Fast Generative Forecasting of Multivariate Trajectories
Oct 15, 2026Jiachen Yao, Xiyuan Zhang, Annan Yu +5Time Series Forecasting Multivariate Time Series Forecasting
California Institute of Technology · Amazon
CGDT-CESN: Correlation-Guided and Dynamics-Trained Clustered Echo State Network for Multivariate Time-Series Prediction
Oct 15, 2026Hariharan Srinivasan, Masoom Bhargava, Sayan GuptaMultivariate Time Series Forecasting Time Series Forecasting
The Uncertainty Lab, Department of Applied Mechanics & Biomedical Engineering, Indian Institute of Technology Madras, India - 600036 · Center for Complex Systems & Dynamics IIT Madras, India - 600036
DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting
Oct 15, 2026Yaohua Zha, Hai Wu, Shu-Tao XiaTime Series Forecasting Multivariate Time Series Forecasting
Pengcheng Laboratory · Tsinghua Shenzhen International Graduate School, Tsinghua University
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Oct 14, 2026Chengyi Pan, Huangqimei Zheng, Hao XueTime Series Modeling Exogenous Time Series Forecasting
UGOD, HKUST-GZ · School of Software, Yunnan University · UGOD, The Hong Kong University of Science and Technology (Guangzhou)
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
Oct 13, 2026Andrei VelichkoTime-Series Causal Discovery Multivariate Time Series Forecasting
Institute of Physics and Technology, Petrozavodsk State University, Petrozavodsk, Russia
Hierarchical Time Series Forecasting with Operator-Valued Kernels
Oct 13, 2026Junjie Yang, Adeline Fermanian, Claire Boyer +1Time Series Forecasting Kernel Methods
LPSM · LMO, IUF
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
Oct 13, 2026Yangze Li, Zhengyang Zhou, Qihe Huang +3Long-Term Time Series Forecasting Multivariate Time Series Forecasting
University of Science and Technology of China (USTC), Hefei, China · Suzhou Institute for Advanced Research, USTC, Suzhou, China
IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models
Oct 12, 2026Vebjørn Haugland, Pavel Gulin, David Foss +5Time Series Forecasting Time-Series Foundation Models
Cognite

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IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting

Anushka Katiyar, Cong Feng, Jing Wang, Tristan Liu+2

National Laboratory of the Rockies · Latimer Controls

Solar Power Forecasting Probabilistic Time Series Forecasting Multivariate Time Series Forecasting

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This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. The forecasting model is built on a four-branch architecture that separately encodes plant-level temporal history and inverter-level spatial cloud dynamics through a graph attention encoder with a learned adjacency, known-future solar geometry, and the current meteorological state, fused through gated cross-attention. The geometry branch adds two forward-looking physical signals, spatial cloud heterogeneity and inverter ramp-down activity, each empirically weighted toward the horizon where it is most predictive. The decoder anchors its prediction to a smart persistence baseline, learning a horizon-conditioned correction, and the resulting quantile forecasts are calibrated online through a kernel-weighted adaptive conformal calibration step. The proposed model outperforms all 14 baselines, achieving a forecast skill score (FSS) of 0.2950, 0.099 above the next-best model. A branch-composition ablation shows that every branch contributes positively and the model retains substantial skill in challenging overcast and variable sky conditions (FSS 0.198 and 0.255).