Multivariate Time Series Forecasting

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46 papers in the last four weeks, up 39% on the four weeks before. 0.3% of all new papers.

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XCTFormer: Leveraging Cross-Channel and Cross-Time Dependencies for Enhanced Time-Series Analysis
May 18, 2026Israel Zexer, Omri AzencotMultivariate Time Series Forecasting Time Series Imputation
The Stein Faculty of Computer and Information Science Ben-Gurion University of the Negev
QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
May 18, 2026Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan +2Multivariate Time Series Forecasting Hybrid Quantum-Classical ML
eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
May 18, 2026Robson W. S. Pessoa, Julien Amblard, Alessandra Russo +1Multivariate Time Series Forecasting Industrial Anomaly Detection
aDepartment of Chemical Engineering, Norwegian University of Science and Technology2026 (NTNU), Trondheim, Norway · bDepartment of Computing, Imperial College London, London, United Kingdom
L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
May 18, 2026Fan Zhang, Shijun Chen, Hua WangMultivariate Time Series Forecasting Latent Dynamics Modeling
Shandong Technology and Business University, Yantai, Shandong, China · Ludong University, Yantai, Shandong, China
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
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 Forecasting Long-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
Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics
May 14, 2026Yunfei Luo, Xi Chen, Yuliang Chen +8Multivariate Time Series Forecasting World Model Learning
University of California San Diego · Dartmouth College · Amazon Web Services +3
PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting
May 14, 2026Saad Masrur, Yuxuan Jiang, Matti Hiltunen +2Multivariate Time Series Forecasting Wireless Communications
AT&T RAN Technology, Bedminster, NJ · Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
May 14, 2026Manrui Jiang, Jingru Huang, Yong Chen +1Multivariate Time Series Forecasting Reinforcement Learning
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China · Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA.
SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies
May 14, 2026Hao Li, Lu Zhang, Liu Chong +3Multivariate Time Series Forecasting Non-Stationary Time Series Forecasting
1Sichuan University · 2Chengdu University of Information Technology · 3McGill University +1
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
May 13, 2026Mohammad R. Rezaei, Tejas Balaji, Rahul G. KrishnanIrregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computer Science University of Toronto Vector Institute Toronto, ON, Canada
Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
May 13, 2026Zhenan Yu, Guangxin Jiang, Jin YangMultivariate Time Series Forecasting Long-Term Time Series Forecasting
1Harbin Institute of Technology
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
May 12, 2026Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4Multivariate Time Series Forecasting Time Series Forecasting
EDF R&D, Palaiseau · Inria Sophia-Antipolis, Université Côte d’Azur · Université Paris Cit'e
EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting
May 12, 2026Madhurima Panja, Danny D'Agostino, Huitao Li +2Multivariate Time Series Forecasting Epidemic Forecasting
Sorbonne University Abu Dhabi · Duke-NUS Medical School, Singapore
Multivariate Financial Forecasting using the Chronos Time Series Foundation Models
May 8, 2026Sanjiv R Das, Tarang Goyal, Mohini YadavMultivariate Time Series Forecasting Quantitative Finance
Santa Clara University
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
May 8, 2026Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-ThiemeMultivariate Time Series Forecasting Time Series Forecasting
1ISMLL, University of Hildesheim, VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany.
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
May 8, 2026Fan Zhang, Shiming Fan, Hua WangMultivariate Time Series Forecasting Feature Interaction Modeling
Shandong Technology and Business University, Yantai, Shandong, China · Ludong University, Yantai, Shandong, China
SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces
May 7, 2026Zeyuan Ding, Lingfeng Zheng, Dian Ding +1Multivariate Time Series Forecasting Time Series Forecasting
1Shanghai Jiao Tong University, Shanghai, China · 2Shaanxi Normal University, Xi’an, China
Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
May 6, 2026Ruichao Guo, Xingyao Han, Luo Wenshui +3Multivariate Time Series Forecasting Conformal Prediction
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China
PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
May 1, 2026Yingbo Zhou, Yutong Ye, Zhiwei Ling +5Multivariate Time Series Forecasting Time Series Forecasting
Fudan University · Beihang University · Zhejiang University +1
Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
Apr 30, 2026Daniel Azenkot, Michael Fire, Eran Ben EliaMultivariate Time Series Forecasting Time Series Forecasting
Faculty of Computer and Information Science, University of Ben Gurion, Be’er Sheva, Israel · Department of Geography and Environmental Development, Ben-Gurion, Israel
ITS-Mina: A Harris Hawks Optimization-Based All-MLP Framework with Iterative Refinement and External Attention for Multivariate Time Series Forecasting
Apr 30, 2026Pourya Zamanvaziri, Amirhossein Sadr, Aida Pakniyat +1Multivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science and Engineering, Shahid Beheshti University, Iran · School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Iran
Simulating clinical interventions with a generative multimodal model of human physiology
Apr 30, 2026Guy Lutsker, Gal Sapir, Jordi Merino +7Multivariate Time Series Forecasting Healthcare
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. · Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. · NVIDIA, Tel Aviv, Israel. +5
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
Apr 30, 2026Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-ThiemeIrregular Time-Series Modeling Multivariate Time Series Forecasting
1ISMLL, University of Hildesheim, Germany
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
Apr 27, 2026Ankan Basu, Jyotiraditya Roy, Aditya Datta +2Multivariate Time Series Forecasting Time Series Forecasting
Positional Astronomy Centre, India Meteorological Department, Kolkata, India · Department of Computer Science and Engineering, Jadavpur University, Kolkata, India · Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Heritage Institute of Technology, Kolkata, India +2
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
The CTLNet for Shanghai Composite Index Prediction
Apr 18, 2026Haibin JiaoMultivariate Time Series Forecasting Hybrid CNN-Transformer Architectures
Independent Researcher
Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data
Apr 16, 2026Aleksander Berezowski, Hassan Hassanzadeh, Gouri GindeMultivariate Time Series Forecasting Masked Autoencoders
Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary Calgary, Canada · Schulich School of Engineering Department of Chemical and Petroleum Engineering University of Calgary Calgary, Canada
Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
Apr 16, 2026Esther Brown, Victoria Dean, Finale Doshi-VelezMultivariate Time Series Forecasting Wearable Health Monitoring
Harvard University, Cambridge, MA · Princeton University, Princeton, NJ
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

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XCTFormer: Leveraging Cross-Channel and Cross-Time Dependencies for Enhanced Time-Series Analysis

Israel Zexer, Omri Azencot

The Stein Faculty of Computer and Information Science Ben-Gurion University of the Negev

Multivariate Time Series Forecasting Time Series Imputation Cross-Attention Time Series Transformers

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Multivariate time-series analysis involves extracting informative representations from sequences of multiple interdependent variables, supporting tasks such as forecasting, imputation, and anomaly detection. In real-world scenarios, these variables are typically collected from a shared context or underlying phenomenon, suggesting the presence of latent dependencies across time and channels that can be leveraged to improve performance. However, recent findings show that channel-independent (CI) models, which assume no inter-variable dependencies, often outperform channel-dependent (CD) models that explicitly model such relationships. This surprising result indicates that current CD models may not fully exploit their potential due to limitations in how dependencies are captured. Recent studies have revisited channel dependence modeling with various approaches; however, these methods often employ indirect modeling strategies, which can lead to meaningful dependencies being overlooked. To address this issue, we introduce XCTFormer, a transformer-based channel-dependent (CD) model that explicitly captures cross-temporal and cross-channel dependencies via an enhanced attention mechanism. The model operates in a token-to-token fashion, modeling pairwise dependencies between every pair of tokens across time and channels. The architecture comprises (i) a data processing module, (ii) a novel Cross-Relational Attention Block (CRAB) that increases capacity and expressiveness, and (iii) an optional Dependency Compression Plugin (DeCoP) that improves scalability. Through extensive experiments on three time-series benchmarks, we show that XCTFormer achieves strong results compared to widely recognized baselines; in particular, it attains state-of-the-art performance on the imputation task, outperforming the second-best method by an average of 20.8% in MSE and 15.3% in MAE.

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