Time Series

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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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AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
May 24, 2026Rui Wang, Renhao Xue, Ray Razi +2Time Series Foundation Models Time Series
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
May 24, 2026Hongkai Li, Shifeng Xie, Lefei Shen +7Time Series Foundation Models Time Series
MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
May 24, 2026Da Zhang, Bingyu Li, Zhiyuan Zhao +3Time-Series Classification State Space Models
Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
May 24, 2026Jinjin Chi, Lei Feng, Lulu Zhang +6Zero-Shot Forecasting Factor
A comparative study of accuracy and rollout stability of temporal surrogate models
May 24, 2026Rajarshi BiswasLong-Horizon Forecasting Surrogate Models
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
May 23, 2026Liying Han, Kang Yang, Oliver Wang +9Closed-Book Question Answering Time Series
Assessing the Operational Viability of Foundation Models for Time Series Forecasting
May 23, 2026Kavin Soni, Debanshu Das, Vamshi GuduguntlaTime Series Foundation Models Wireless Foundation Models
Valid and Expressive Copulas for Irregular Multivariate Time Series
May 22, 2026Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1Gaussian Copula Multivariate Time Series Forecasting
Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
May 22, 2026Jinglin Li, Jun Tan, QI Fang +1Non-Stationarity Time Series
PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
May 22, 2026Junru Zhang, Lang Feng, Jinbo Wang +6Synthetic Time Series Multiscale Dynamics
Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
May 22, 2026Musheng Li, Ziying Zhang, Cheng jin +1Time Series Sequence Modeling
fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Brain Magnetic Resonance Imaging Depression Detection
PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows
May 22, 2026Minju Kim, Youngbum HurMultivariate Forecasting Time Series
World Machine: Towards Generative World Modeling for Time-Series
May 21, 2026Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini +2Generative Models Unified Transformer
ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
May 21, 2026José Alberto Rodríguez, Luis Balderas, Miguel Lastra +2Time-Series Classification Time Series
Forecasting Scientific Progress with Artificial Intelligence
May 21, 2026Sean Wu, Pan Lu, Yupeng Chen +7Scientific Discovery Load Forecasting
Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most
May 21, 2026Nick Merrill, Jaeho Lee, Ezra KargerLoad Forecasting Epidemic Forecasting
Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
May 21, 2026Xianhao Song, Yuang Zhang, Yuqi She +2Time-Series Classification Time Series
CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification
May 21, 2026Fan Zhang, Yating Cui, Hua WangTime-Series Classification Spatiotemporal Representations
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
May 21, 2026Jialin Chen, Aosong Feng, Harshit Verma +7Load Forecasting Large Language Model Reasoning
Divide and Contrast: Learning Robust Temporal Features without Augmentation
May 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorTemporal Deep Learning Framework Synthetic Data Augmentation
SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure
May 20, 2026Chen Su, Pengsen Cheng, Yuanhe Tian +1Social Media Time Series
Reviving Error Correction in Modern Deep Time-Series Forecasting
May 20, 2026Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3Multivariate Forecasting Time Series
Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series
May 20, 2026Andi XuAlgorithm Auditing Time Series
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
May 19, 2026Alexis Roger, Prateek Humane, Zhenghan Tai +4Generative Pretrained Transformers Time Series
Toto 2.0: Time Series Forecasting Enters the Scaling Era
May 19, 2026Emaad Khwaja, Chris Lettieri, Gerald Woo +10Time Series Foundation Models Time Series
INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification
May 19, 2026Seongjun Lee, Seokhyun Lee, Changhee LeeTime-Series Classification Statistical Shape Model
Latent Laplace Diffusion for Irregular Multivariate Time Series
May 19, 2026Zinuo You, Jin Zheng, John CartlidgeLong-Horizon Forecasting Time Series
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
May 19, 2026David Huk, Dongshan Wang, Miha BresarGaussian Copula Multivariate Time Series Forecasting
TreeText-CTS: Compact, Source-Traceable Tree-Path Evidence for Irregular Clinical Time-Series Prediction
May 19, 2026Kwanhyung Lee, Juhwan Choi, Jongheon Kim +3Electronic Health Records Time-Series Classification
Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models
May 19, 2026Noam Major, Kathy Razmadze, Yoli ShavitTime Series Foundation Models Self-Supervised
Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
May 19, 2026Wenpin Tang, Nizar Touzi, Zikun Zhang +1Stochastic Differential Equations Diffusion Dynamics
An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
May 19, 2026Sooraj Sunil, Balakumar BalasingamTime-Series Classification Bidirectional Long Short-Term Memory
Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
May 18, 2026Paul Quinlan, Jeremy Levasseur, Qingguo Li +1Time Series Foundation Models Time Series
Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting
May 18, 2026Sumit Laha, Ankit Sharma, Hassan ForooshEnergy Forecasting Photovoltaic
KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture
May 18, 2026Luis Balderas, José Alberto Rodríguez, Miguel Lastra +2Time Series Foundation Models Time Series
XCTFormer: Leveraging Cross-Channel and Cross-Time Dependencies for Enhanced Time-Series Analysis
May 18, 2026Israel Zexer, Omri AzencotMultivariate Time Series Forecasting Temporal Dependence
Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
May 18, 2026Jordan Tschida, Matthew Yohe, Edward Kane +10Multimodal Physiological Signals Time-Series Classification
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 +1Anomaly Detection Multivariate Forecasting
Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
May 18, 2026Bo Chen, Yitong Fan, Jie Yao +1Turbulence Long-Horizon Forecasting
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
May 18, 2026Masahiro Suzuki, Bohui Xia, Hiroto Yamamoto +1Synthetic Time Series Time Series
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
May 18, 2026Chenxi Wang, Xiaorong Wang, Peiyang Li +1Synthetic Time Series Time Series
L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
May 18, 2026Fan Zhang, Shijun Chen, Hua WangLlm-Based Forecasting Methods Multivariate Forecasting
Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
May 17, 2026Jingru Fei, Kun Yi, Alex Xing Wang +3Time Series Foundation Models Model Pretraining
Towards Principled Test-Time Adaptation for Time Series Forecasting
May 17, 2026Haochun Wang, Ruichen Xu, Georgios Kementzidis +3Continual Test-Time Adaptation Multivariate Forecasting
Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
May 16, 2026Qian Jiang, Liping SunLlm-Based Forecasting Methods
Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
May 16, 2026Chi Bui, Maria Margarida Mascarenhas, Arnaud Verstraeten +1Electricity Price Forecasting Time Series Foundation Models
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
May 16, 2026Wei Tang, Jinpei Han, Kangning Cui +10Pediatric-Adult Electrocardiogram Alignment Electrocardiogram Classification
CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
May 16, 2026Jiecheng Lu, Jieqi Di, Runhua Wu +1Time Series Causal Reasoning
PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes
May 16, 2026Phat Tran, Neville Mehta, Clara Mosquera-Lopez +3Continuous Glucose Monitoring Long-Horizon Forecasting
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
May 15, 2026Dan Assouline, Erwan Koch, Federico Amato +5Dynamical Downscaling Load Forecasting
ITGPT: Generative Pretraining on Irregular Timeseries
May 15, 2026Antoine Honoré, Ming XiaoTime-Series Classification Time Series
Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification
May 15, 2026Mónika Farsang, Ramin Hasani, Daniela Rus +1Time-Series Classification State Space Models
FRWKV+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting
May 15, 2026Qingyuan Yang, Dongyue Chen, Da Teng +3Multivariate Time Series Forecasting Multivariate Forecasting
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
Hierarchical Two-Stage Framework for Environment-Aware Long-Horizon Vessel Trajectory Prediction
May 15, 2026Ganeshaaraj Gnanavel, Tharindu Fernando, Sridha Sridharan +1Long-Horizon Forecasting Trajectory Prediction
Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
May 14, 2026Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim +1Patient Trajectories Electronic Health Records
TopoPrimer: The Missing Topological Context in Forecasting Models
May 14, 2026Zara Zetlin, Kayhan Moharreri, Maria SafiTime Series Topological Data Analysis
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-Stationarity
Nexus : An Agentic Framework for Time Series Forecasting
May 14, 2026Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6Time Series Foundation Models Agentic Tool Use

May 24, 2026 · cs.LGJ/K move · Enter open · S save

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting

Rui Wang, Renhao Xue, Ray Razi, Huan Song+1

Amazon Web Services

Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a shared dense computation path despite substantial heterogeneity in temporal structure. Mixture-of-Experts (MoE) offers a natural alternative by enabling conditional computation, but standard MoE routing leaves expert specialization weakly identified and often unstable during downstream adaptation. We propose AME-TS, a structure-guided sparse time series foundation model that aligns expert routing with interpretable temporal structure. AME-TS first uses a lightweight regime predictor to estimate series-level descriptors, including forecastability, seasonality, trend, and sparsity, and maps them to a soft structural prior over experts. This series-level prior guides token-level routing during training, encouraging structure-aligned specialization. On the GIFT-Eval benchmark, AME-TS delivers a strong accuracy-efficiency tradeoff across model scales: it substantially outperforms existing time series foundation models at small model scales and remains competitive with the strongest models at larger scales, while activating substantially fewer parameters through sparse routing. We further show that AME-TS learns more interpretable routing geometry and substantially more stable expert specialization than standard MoE during fine-tuning on the M5 dataset. These results suggest that structure-aware routing is an effective and reliable way to realize the benefits of sparse expert models for time series forecasting.

Time Series Foundation Models Time Series Mixture-of-Experts Models

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