|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|-----------|
| FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting OR | applications->time series | Poster |
| Sundial: A Family of Highly Capable Time Series Foundation Models OR | deep learning->foundation models | Oral |
| K\^2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting OR | deep learning->sequential models time series | Spotlight |
| Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting OR | social aspects->privacy | Spotlight |
| CFPT: Empowering Time Series Forecasting through Cross-Frequency Interaction and Periodic-Aware Timestamp Modeling OR | applications->time series | Poster |
| TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state OR | applications->time series | Poster |
| Conditional Diffusion Model with Nonlinear Data Transformation for Time Series Forecasting OR | deep learning->generative models and autoencoders | Poster |
| Non-stationary Diffusion For Probabilistic Time Series Forecasting OR | deep learning->sequential models time series | Spotlight |
| Winner-takes-all for Multivariate Probabilistic Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| Arrow: Accelerator for Time Series Causal Discovery with Time Weaving OR | general machine learning->causality | Poster |
| Spectral-Aware Reservoir Computing for Fast and Accurate Time Series Classification OR | general machine learning->sequential network and time series modeling | Poster |
| TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation OR | general machine learning->sequential network and time series modeling | Poster |
| Channel Normalization for Time Series Channel Identification OR | deep learning->sequential models time series | Poster |
| A Non-isotropic Time Series Diffusion Model with Moving Average Transitions OR | applications->time series | Poster |
| TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting OR | applications->time series | Spotlight |
| TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation OR | applications->time series | Spotlight |
| Lightweight Online Adaption for Time Series Foundation Model Forecasts OR | deep learning->sequential models time series | Poster |
| TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer OR | deep learning->sequential models time series | Poster |
| KAN-AD: Time Series Anomaly Detection with Kolmogorov--Arnold Networks OR | general machine learning->sequential network and time series modeling | Poster |
| KoNODE: Koopman-Driven Neural Ordinary Differential Equations with Evolving Parameters for Time Series Analysis OR | general machine learning->sequential network and time series modeling | Poster |
| Hi-Patch: Hierarchical Patch GNN for Irregular Multivariate Time Series OR | applications->time series | Poster |
| Retrieval Augmented Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series OR | deep learning->sequential models time series | Poster |
| Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series OR | applications->neuroscience cognitive science | Poster |
| CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations OR | applications->time series | Poster |
| TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning OR | applications->time series | Poster |
| A Closer Look at Transformers for Time Series Forecasting: Understanding Why They Work and Where They Struggle OR | deep learning->sequential models time series | Poster |
| Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization OR | deep learning->sequential models time series | Poster |
| LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization OR | deep learning->sequential models time series | Poster |
| LightGTS: A Lightweight General Time Series Forecasting Model OR | deep learning->sequential models time series | Poster |
| Patch-wise Structural Loss for Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters OR | general machine learning->sequential network and time series modeling | Poster |
| Relational Conformal Prediction for Correlated Time Series OR | general machine learning->sequential network and time series modeling | Poster |
| FIC-TSC: Learning Time Series Classification with Fisher Information Constraint OR | applications->time series | Poster |
| HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting OR | applications->time series | Poster |
| ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset OR | applications->time series | Poster |
| VerbalTS: Generating Time Series from Texts OR | applications->time series | Poster |
| LAST SToP for Modeling Asynchronous Time Series OR | deep learning->sequential models time series | Poster |
| LSCD: Lomb--Scargle Conditioned Diffusion for Time series Imputation OR | deep learning->sequential models time series | Poster |
| Time Series Representations with Hard-Coded Invariances OR | general machine learning->sequential network and time series modeling | Poster |
| Causal Discovery from Conditionally Stationary Time Series OR | probabilistic methods->structure learning | Poster |
| WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting OR | applications->time series | Poster |
| Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts OR | applications->time series | Poster |
| Temporal Query Network for Efficient Multivariate Time Series Forecasting OR | applications->time series | Poster |
| Efficient Time Series Processing for Transformers and State-Space Models through Token Merging OR | deep learning->sequential models time series | Poster |
| IMTS is Worth Time \\times Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction OR | deep learning->sequential models time series | Poster |
| LETS Forecast: Learning Embedology for Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| TimeStacker: A Novel Framework with Multilevel Observation for Capturing Nonstationary Patterns in Time Series Forecasting OR | deep learning->sequential models time series | Poster |
| TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation OR | deep learning->sequential models time series | Poster |
| Exploring Representations and Interventions in Time Series Foundation Models OR | applications->time series | Poster |
| Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling OR | general machine learning->sequential network and time series modeling | Poster |
| AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting OR | applications->time series | Poster |