{"total":88,"rows":[{"id":"2410.10393","title":"GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation","created":"2024-10-14","cited_by_count":212,"area":null},{"id":"2412.07236","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","created":"2024-12-10","cited_by_count":199,"area":null},{"id":"2412.03104","title":"ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning","created":"2024-12-04","cited_by_count":103,"area":"lr"},{"id":"2505.14766","title":"This Time is Different: An Observability Perspective on Time Series Foundation Models","created":"2025-05-20","cited_by_count":68,"area":null},{"id":"2502.04395","title":"Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting","created":"2025-02-06","cited_by_count":63,"area":"mllm"},{"id":"2503.07649","title":"TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster","created":"2025-03-06","cited_by_count":40,"area":"rag"},{"id":"2503.01013","title":"TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop","created":"2025-03-02","cited_by_count":39,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2509.22295","title":"Aurora: Towards Universal Generative Multimodal Time Series Forecasting","created":"2025-09-26","cited_by_count":29,"area":null},{"id":"2502.08869","title":"Harnessing Vision Models for Time Series Analysis: A Survey","created":"2025-02-13","cited_by_count":26,"area":null},{"id":"2502.15637","title":"Mantis: Lightweight Foundation Model for Time Series Classification","created":"2025-02-21","cited_by_count":24,"area":null},{"id":"2506.11029","title":"Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model","created":"2025-05-20","cited_by_count":24,"area":"lr"},{"id":"2604.05550","title":"AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery","created":"2026-04-07","cited_by_count":23,"area":null},{"id":"2505.02417","title":"T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models","created":"2025-05-05","cited_by_count":20,"area":"diffusion"},{"id":"2502.12920","title":"Lightweight Online Adaption for Time Series Foundation Model Forecasts","created":"2025-02-18","cited_by_count":16,"area":null},{"id":"2505.17871","title":"BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models","created":"2025-05-23","cited_by_count":16,"area":null},{"id":"2510.13654","title":"Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges","created":"2025-10-15","cited_by_count":16,"area":"lr"},{"id":"2503.11411","title":"Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models","created":"2025-03-14","cited_by_count":15,"area":"synth"},{"id":"2508.02879","title":"CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data","created":"2025-08-04","cited_by_count":13,"area":"synth"},{"id":"2502.17812","title":"Can Multimodal LLMs Perform Time Series Anomaly Detection?","created":"2025-02-25","cited_by_count":12,"area":"mllm"},{"id":"2508.04379","title":"VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones","created":"2025-08-06","cited_by_count":12,"area":null},{"id":"2510.03519","title":"TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning","created":"2025-10-03","cited_by_count":12,"area":"lr"},{"id":"2602.06909","title":"Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models","created":"2026-02-06","cited_by_count":12,"area":null},{"id":"2602.17868","title":"MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies","created":"2026-02-19","cited_by_count":11,"area":null},{"id":"2501.15942","title":"TimeHF: Billion-Scale Time Series Models Guided by Human Feedback","created":"2025-01-27","cited_by_count":10,"area":"alignment"},{"id":"2502.12944","title":"Performance of Zero-Shot Time Series Foundation Models on Cloud Data","created":"2025-02-18","cited_by_count":10,"area":null},{"id":"2502.21245","title":"TimesBERT: A BERT-Style Foundation Model for Time Series Understanding","created":"2025-02-28","cited_by_count":10,"area":null},{"id":"2509.20823","title":"CaTS-Bench: Can Language Models Describe Time Series?","created":"2025-09-25","cited_by_count":10,"area":"lr"},{"id":"2502.14887","title":"Vision-Enhanced Time Series Forecasting via Latent Diffusion Models","created":"2025-02-16","cited_by_count":9,"area":"diffusion"},{"id":"2505.24003","title":"Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2509.25826","title":"Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models","created":"2025-09-30","cited_by_count":9,"area":null},{"id":"2606.14222","title":"Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models","created":"2026-06-12","cited_by_count":9,"area":null},{"id":"2510.21795","title":"Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention","created":"2025-10-20","cited_by_count":8,"area":null},{"id":"2505.24030","title":"From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?","created":"2025-05-29","cited_by_count":7,"area":null},{"id":"2602.17149","title":"TimeOmni-VL: Unified Models for Time Series Understanding and Generation","created":"2026-02-19","cited_by_count":7,"area":null},{"id":"2504.09993","title":"AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification","created":"2025-04-14","cited_by_count":6,"area":null},{"id":"2505.18570","title":"VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis","created":"2025-05-24","cited_by_count":6,"area":null},{"id":"2506.08641","title":"Unlocking Pretrained Vision Transformers for Time Series Classification","created":"2025-06-10","cited_by_count":6,"area":null},{"id":"2502.01922","title":"LAST SToP For Modeling Asynchronous Time Series","created":"2025-02-04","cited_by_count":5,"area":null},{"id":"2510.07513","title":"MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis","created":"2025-10-08","cited_by_count":5,"area":"mllm"},{"id":"2605.31580","title":"Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings","created":"2026-05-29","cited_by_count":5,"area":null},{"id":"2502.14704","title":"Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting","created":"2025-02-20","cited_by_count":4,"area":null},{"id":"2604.11529","title":"TempusBench: An Evaluation Framework for Time-Series Forecasting","created":"2026-04-13","cited_by_count":4,"area":null},{"id":"2606.05878","title":"TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning","created":"2026-06-04","cited_by_count":4,"area":null},{"id":"2502.19960","title":"SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model","created":"2025-02-27","cited_by_count":3,"area":null},{"id":"2509.04208","title":"One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo","created":"2025-09-04","cited_by_count":3,"area":null},{"id":"2512.14253","title":"FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2602.05646","title":"Empowering Time Series Analysis with Large-Scale Multimodal Pretraining","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2602.07830","title":"Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning","created":"2026-02-08","cited_by_count":3,"area":"lr"},{"id":"2605.00015","title":"TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2605.26161","title":"TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models","created":"2026-05-24","cited_by_count":3,"area":null},{"id":"2609.04239","title":"EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series","created":"2026-08-04","cited_by_count":3,"area":null},{"id":"2505.22815","title":"IMTS is Worth Time $\\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2507.23253","title":"Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2508.11954","title":"UniCast: A Unified Framework for Instance-Conditioned Multimodal Time-Series Forecasting","created":"2025-08-16","cited_by_count":2,"area":null},{"id":"2509.15105","title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","created":"2025-09-18","cited_by_count":2,"area":"moe"},{"id":"2510.09780","title":"SVTime: Small Time Series Forecasting Models Informed by \"Physics\" of Large Vision Model Forecasters","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2511.23260","title":"Time Series Forecasting via Direct Per-Step Probability Distribution Modeling","created":"2025-11-28","cited_by_count":2,"area":null},{"id":"2602.03026","title":"Visual Reasoning over Time Series via Multi-Agent System","created":"2026-02-03","cited_by_count":2,"area":"lr"},{"id":"2604.06727","title":"Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2607.08940","title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning","created":"2026-07-09","cited_by_count":2,"area":"lr"},{"id":"2510.06200","title":"StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2602.14024","title":"EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models","created":"2026-02-15","cited_by_count":1,"area":null},{"id":"2603.05997","title":"MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2603.07572","title":"TS-MLLM: A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis","created":"2026-03-08","cited_by_count":1,"area":"mllm"},{"id":"2605.12550","title":"SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting","created":"2026-05-10","cited_by_count":1,"area":null},{"id":"2506.22498","title":"ViFusionTST: Deep Fusion of Time-Series Image Representations from Load Signals for Early Bed-Exit Prediction","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2509.17802","title":"TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2510.03244","title":"VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2511.19856","title":"Temporal-Visual Semantic Alignment: A Unified Architecture for Transferring Spatial Priors from Vision Models to Zero-Shot Temporal Tasks","created":"2025-11-25","cited_by_count":0,"area":"mllm"},{"id":"2602.04769","title":"NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2602.16681","title":"VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2602.23528","title":"Neural Operators Can Discover Functional Clusters","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.26842","title":"VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2604.16748","title":"TriTS: Time Series Forecasting from a Multimodal Perspective","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.20449","title":"LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2606.09861","title":"Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.13024","title":"CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2608.20005","title":"Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2608.23473","title":"MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters","created":"2026-08-24","cited_by_count":0,"area":"agents"},{"id":"2608.23855","title":"In-Context Inpainting for Time Series Forecasting","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.12639","title":"Explaining Time Series Forecasting with Horizon-Resolved Attribution","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.20156","title":"QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.22820","title":"Beyond Average Error through Oracle-Informed Stress Tests for Time-Series Forecasting","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2609.24441","title":"MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.30880","title":"EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.34511","title":"HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.04786","title":"KALEIDO: Input-Space Adaptation of a Vision Model for Time-Series Forecasting Through Gated Fold Geometries","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}