Time Series

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Period ending 2026-09-21

21 new papers

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Time Series

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Time Series Foundation Models

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Time-Series Classification

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Long-Horizon Forecasting

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Multivariate Forecasting

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Multivariate Time Series Forecasting

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Load Forecasting

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Synthetic Time Series

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Llm-Based Forecasting Methods

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Zero-Shot Forecasting

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654 papers

Latest in Time Series

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Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
Mar 23, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +7Multimodal Fusion Time Series
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Mar 16, 2026Seunghan Lee, Jaehoon Lee, Jun Seo +7Zero-Shot Forecasting Retrieval-Augmented Generation Systems
Overcoming the Modality Gap in Context-Aided Forecasting
Mar 12, 2026Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4Llm-Based Forecasting Methods Synthetic Data Augmentation
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
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Deep Learning Network-Temporal Models For Traffic Prediction
Mar 12, 2026Yufeng Xin, Ethan FanAccurate Traffic Forecasting Multivariate Time Series Forecasting
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 Time Series
An intuitive rearranging of the Yates covariance decomposition for probabilistic verification of forecasts with the Brier score
Mar 4, 2026Bruno Hebling VieiraLoad Forecasting Provable Guarantees
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Learning with the Nash-Sutcliffe loss
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Generative Modeling via Kernelized Stochastic Interpolants
Feb 23, 2026Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar +2Stochastic Differential Equations Generative Models
Adaptive Time Series Reasoning via Segment Selection
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SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
Feb 18, 2026Xu Zhang, Qitong Wang, Peng Wang +1Multivariate Time Series Forecasting Multiscale
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Feb 15, 2026Nicolas Zumarraga, Thomas Kaar, Ning Wang +12Longbench Time Series
Capture Timing-Attention of Events in Clinical Time Series
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Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
Feb 4, 2026Irene Iele, Giulia Romoli, Daniele Molino +4Weather Models Spatio-Temporal Forecasting
DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
Feb 3, 2026Aijie Shu, Wenbin Wu, Gbenga Ibikunle +1Decentralized Finance Credit Risk Prediction
HALT: Hallucination Assessment via Log-probs as Time series
Feb 2, 2026Ahmad Shapiro, Karan Taneja, Ashok GoelHallucination Detection Hallucination Rate
Universal Redundancies in Time Series Foundation Models
Feb 2, 2026Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai +1Time Series Foundation Models Synthetic Time Series
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 Time Series
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
Feb 1, 2026Jinju Park, Seokho KangTime-Series Anomaly Detection Time Series
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Jan 31, 2026Juntao Fang, Shifeng Xie, Shengbin Nie +7Time-Series Classification Zero-Shot Inference
Synthetic Time Series Generation via Complex Networks
Jan 30, 2026Jaime Vale, Vanessa Freitas Silva, Maria Eduarda Silva +1Synthetic Time Series Quantile Regression
Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
Jan 23, 2026Haonan Yang, Jianchao Tang, Zhuo LiTime Series Prior Knowledge
Finite-Sample Unbiased Variance of MMD under Unbalanced Sampling: Exact Estimation and Quasi-Linear Computation
Jan 20, 2026Shijie Zhong, Yikun Yang, Da Gong +1Maximum Mean Discrepancy Finite-Sample
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Jan 20, 2026Xu Zhang, Junwei Deng, Chang Xu +2Synthetic Time Series Neural Ordinary Differential Equations
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
Jan 8, 2026Shovito Barua Soumma, Hassan GhasemzadehContinuous Glucose Monitoring Llm-Based Forecasting Methods
Modeling Information Blackouts in Missing Not-At-Random Time Series Data
Jan 4, 2026Aman Sunesh, Allan Ma, Siddarth NilolAccurate Traffic Forecasting Missing Data
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
Dec 3, 2025Florent Forest, Amaury Wei, Olga FinkTime Series Interpretable Models
KAN vs LSTM Performance in Time Series Forecasting
Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Multivariate Forecasting Kolmogorov-Arnold Networks
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Nov 19, 2025Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22Wireless Foundation Models Foundation Model
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Nov 13, 2025YongKyung Oh, Dong-Young Lim, Sungil KimTime-Series Classification Invertible Flows
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Oct 1, 2025Nouha Karaouli, Denis Coquenet, Elisa Fromont +2Time Series Foundation Models Catastrophic Forgetting
fev-bench: A Realistic Benchmark for Time Series Forecasting
Sep 30, 2025Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen +5Multivariate Forecasting Reproducibility
HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning
Sep 28, 2025Md Mozaharul Mottalib, Thao-Ly T. Phan, Rahmatollah BeheshtiElectronic Health Record Foundation Models Hybrid Transformer
Maximum diversity and weighting for invariants of periodic time series
Sep 14, 2025Byungchang SoTime Series Invariance
TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
Aug 24, 2025YongKyung Oh, Dong-Young Lim, Sungil Kim +1Time-Series Classification Missing Data
L-GTA: Latent Generative Modeling for Time Series Augmentation
Jul 31, 2025Luis Roque, Vitor Cerqueira, Carlos Soares +1Synthetic Data Augmentation Synthetic Time Series
$T^{3}S$ : Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Jun 15, 2025Mehmet Ozgur Turkoglu, Selene Ledain, Jeffrey Zweidler +2Time Series Temporal Scales
Temporal horizons in forecasting: a performance-learnability trade-off
Jun 4, 2025Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2Long-Horizon Forecasting Autoregressive Model
Temporal Variational Implicit Neural Representations
Jun 2, 2025Batuhan Koyuncu, Rachael DeVries, Ole Winther +1Multivariate Time Series Forecasting Time Series
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Synthetic Time Series Stochastic Systems
A Network Science Approach to Granular Time Series Segmentation
May 23, 2025Ivana Kesić, Carolina Fortuna, Mihael Mohorčič +1Time-Series Classification Temporal Graph Neural Networks
Human Supervisor Workload Prediction: Lag Horizon Selection
May 21, 2025Mark-Robin Giolando, Julie A. AdamsLong-Horizon Forecasting Workload
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset
May 15, 2025Chengsen Wang, Qi Qi, Zhongwen Rao +2Zero-Shot Forecasting Multimodal Foundation Model
Mantis: Lightweight Foundation Model for Time Series Classification
Feb 21, 2025Vasilii Feofanov, Songkang Wen, Shifeng Xie +10Time Series Foundation Models Time-Series Classification
Surrogate Modeling for Explainable Predictive Time Series Corrections
Dec 27, 2024Alfredo Lopez, Florian SobieczkyMultivariate Forecasting Surrogate Models
Granger Causality in Extremes
Jul 12, 2024Juraj Bodik, Olivier C. PascheCausal Foundation Models Extremes
Breaking the $T^{2/3}$ Barrier for Sequential Calibration
Jun 19, 2024Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +3Multicalibration and Calibration Load Forecasting
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Feb 22, 2024YongKyung Oh, Dong-Young Lim, Sungil KimNeural Ordinary Differential Equations Stochastic Differential Equations
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
Jan 10, 2024YongKyung Oh, Dong-Young Lim, Sungil KimTime Series Neural Ordinary Differential Equations
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
Oct 11, 2023Julia Werner, Christoph Gerum, Moritz Reiber +2Video Capsule Endoscopy Convolutional Neural Networks
BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification
Date pendingJingtao Zhang, Yi Liu, Qi Shen +1Hybrid Transformer Time-Series Classification
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
Date pendingLige Zhang, Ali Maatouk, Jialin Chen +3Time Series Mixture-of-Experts Models
How Proper Scoring Rules Shape LLM Forecasting
Date pendingBenjamin Turtel, Paul Wilczewski, Kris Skotheim +2Scoring Load Forecasting

Mar 23, 2026 · cs.LGJ/K move · Enter open · S save

Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion

Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo+6

LG AI Research Seoul, South Korea

Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific datasets or relying on architecture-specific designs that limit generalization. In this paper, we show that multimodal models with naive fusion strategies (e.g., simple addition or concatenation) often underperform unimodal TS models, which we attribute to the uncontrolled integration of auxiliary modalities which may introduce irrelevant information. Motivated by this observation, we explore various constrained fusion methods designed to control such integration and find that they consistently outperform naive fusion methods. Furthermore, we propose Controlled Fusion Adapter (CFA), a simple plug-in method that enables controlled cross-modal interactions without modifying the TS backbone, integrating only relevant textual information aligned with TS dynamics. CFA employs low rank adapters to filter irrelevant textual information before fusing it into temporal representations. We conduct over 20K experiments across various datasets and TS/text models, demonstrating the effectiveness of the constrained fusion methods. Code is available at: https://github.com/seunghan96/cfa.

Multimodal Fusion Time Series Fusion

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