{"total":100,"rows":[{"id":"2503.01875","title":"Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement","created":"2025-02-26","cited_by_count":82,"area":null},{"id":"2511.11698","title":"Moirai 2.0: When Less Is More for Time Series Forecasting","created":"2025-11-12","cited_by_count":69,"area":null},{"id":"2503.16858","title":"MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering","created":"2025-03-21","cited_by_count":55,"area":"lr"},{"id":"2502.08942","title":"Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative","created":"2025-02-13","cited_by_count":40,"area":null},{"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":"2506.20093","title":"ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset","created":"2025-06-25","cited_by_count":37,"area":null},{"id":"2510.02410","title":"OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data","created":"2025-10-02","cited_by_count":36,"area":"lr"},{"id":"2509.24803","title":"TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models","created":"2025-09-29","cited_by_count":27,"area":null},{"id":"2509.11575","title":"A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models","created":"2025-09-15","cited_by_count":21,"area":"lr"},{"id":"2503.11835","title":"How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook","created":"2025-03-14","cited_by_count":20,"area":null},{"id":"2506.10412","title":"Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series","created":"2025-06-12","cited_by_count":19,"area":null},{"id":"2602.13272","title":"TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks","created":"2026-02-05","cited_by_count":17,"area":"agents"},{"id":"2503.13868","title":"Out-of-Distribution Generalization in Time Series: A Survey","created":"2025-03-18","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":"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":"2502.17812","title":"Can Multimodal LLMs Perform Time Series Anomaly Detection?","created":"2025-02-25","cited_by_count":12,"area":"mllm"},{"id":"2509.01822","title":"When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference","created":"2025-09-01","cited_by_count":12,"area":"lr"},{"id":"2509.05215","title":"BEDTime: A Unified Benchmark for Automatically Describing Time Series","created":"2025-09-05","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":"2506.21611","title":"When Does Multimodality Lead to Better Time Series Forecasting?","created":"2025-06-20","cited_by_count":11,"area":null},{"id":"2507.14507","title":"Diffusion Models for Time Series Forecasting: A Survey","created":"2025-07-19","cited_by_count":11,"area":"diffusion"},{"id":"2601.23204","title":"TSAQA: Time Series Analysis Question And Answering Benchmark","created":"2026-01-30","cited_by_count":11,"area":null},{"id":"2503.14190","title":"Inferring Events from Time Series using Language Models","created":"2025-03-18","cited_by_count":10,"area":null},{"id":"2509.24789","title":"Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2504.19669","title":"Multimodal Conditioned Diffusive Time Series Forecasting","created":"2025-04-28","cited_by_count":9,"area":"diffusion"},{"id":"2507.15066","title":"Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback","created":"2025-07-20","cited_by_count":8,"area":null},{"id":"2506.09114","title":"TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval","created":"2025-06-10","cited_by_count":7,"area":null},{"id":"2506.22039","title":"UniCA: Unified Covariate Adaptation for Time Series Foundation Model","created":"2025-06-27","cited_by_count":7,"area":null},{"id":"2507.21830","title":"DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework","created":"2025-07-29","cited_by_count":7,"area":null},{"id":"2509.00687","title":"Text Reinforcement for Multimodal Time Series Forecasting","created":"2025-08-31","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":"2507.10098","title":"Fusing Large Language Models with Temporal Transformers for Time Series Forecasting","created":"2025-07-14","cited_by_count":6,"area":null},{"id":"2602.01588","title":"Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting","created":"2026-02-02","cited_by_count":6,"area":null},{"id":"2604.21199","title":"ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response","created":"2026-04-23","cited_by_count":6,"area":null},{"id":"2510.01116","title":"Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning","created":"2025-10-01","cited_by_count":5,"area":"lr"},{"id":"2603.12451","title":"Overcoming the Modality Gap in Context-Aided Forecasting","created":"2026-03-12","cited_by_count":5,"area":null},{"id":"2603.12664","title":"From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space","created":"2026-03-13","cited_by_count":5,"area":null},{"id":"2603.15452","title":"Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting","created":"2026-03-16","cited_by_count":5,"area":"lr"},{"id":"2507.00914","title":"Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications","created":"2025-07-01","cited_by_count":4,"area":"agents"},{"id":"2509.24378","title":"AXIS: Explainable Time Series Anomaly Detection with Large Language Models","created":"2025-09-29","cited_by_count":4,"area":null},{"id":"2510.01111","title":"TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction","created":"2025-10-01","cited_by_count":4,"area":null},{"id":"2603.01042","title":"Thoth: Mid-Training Bridges LLMs to Time Series Understanding","created":"2026-03-01","cited_by_count":4,"area":null},{"id":"2511.06344","title":"TimeSense:Making Large Language Models Proficient in Time-Series Analysis","created":"2025-11-09","cited_by_count":3,"area":"lr"},{"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":"2603.21612","title":"Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction","created":"2026-03-23","cited_by_count":3,"area":null},{"id":"2606.05404","title":"Harnessing Generalist Agents for Contextualized Time Series","created":"2026-06-03","cited_by_count":3,"area":"agents"},{"id":"2607.20002","title":"Post-Training in Time Series Foundation Models: A Unifying Framework","created":"2026-07-22","cited_by_count":3,"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":"2602.14200","title":"TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning","created":"2026-02-15","cited_by_count":2,"area":"lr"},{"id":"2605.13711","title":"MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling","created":"2026-05-13","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":"2609.15087","title":"Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context","created":"2026-09-14","cited_by_count":2,"area":null},{"id":"2609.24559","title":"$t_0$: A Time-Series Foundation Model for Forecasting with Context","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2502.15466","title":"Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation","created":"2025-02-21","cited_by_count":1,"area":null},{"id":"2505.10083","title":"ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset","created":"2025-05-15","cited_by_count":1,"area":null},{"id":"2506.14786","title":"PIPE: Physics-Informed Position Encoding for Alignment of Satellite Images and Time Series","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2510.20952","title":"LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2512.02833","title":"A Comparative Study on How Data Normalization Affects Zero-Shot Generalization in Time Series Foundation Models","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2512.14718","title":"SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2605.20268","title":"Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2607.25947","title":"A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series","created":"2026-07-28","cited_by_count":1,"area":"lr"},{"id":"2608.05238","title":"Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language","created":"2026-08-05","cited_by_count":1,"area":null},{"id":"2609.34842","title":"QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2506.11512","title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2510.04710","title":"ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts","created":"2025-10-06","cited_by_count":0,"area":null},{"id":"2510.20651","title":"xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion","created":"2025-10-23","cited_by_count":0,"area":"distill"},{"id":"2510.25091","title":"H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts","created":"2025-10-29","cited_by_count":0,"area":"lr"},{"id":"2601.12667","title":"Empowering All-in-Loop Health Management of Spacecraft Power System in the Mega-Constellation Era via Human-AI Collaboration","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2602.20494","title":"KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.05092","title":"Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.11392","title":"Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks","created":"2026-03-12","cited_by_count":0,"area":"agents"},{"id":"2603.19899","title":"Deep Time-Series Forecasting in 10 Years: A Survey","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.18305","title":"CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting","created":"2026-04-20","cited_by_count":0,"area":"lr"},{"id":"2605.28866","title":"Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.18986","title":"Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.24950","title":"MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios","created":"2026-06-23","cited_by_count":0,"area":"lr"},{"id":"2606.26487","title":"Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2607.07951","title":"Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.14510","title":"VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence","created":"2026-07-16","cited_by_count":0,"area":"moe"},{"id":"2607.24419","title":"Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2608.03391","title":"TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.14054","title":"Model-agnostic Retrieval-Augmented Extended Forecasting for time series","created":"2026-08-14","cited_by_count":0,"area":"rag"},{"id":"2608.14270","title":"TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments","created":"2026-08-14","cited_by_count":0,"area":"agents"},{"id":"2608.15291","title":"ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning","created":"2026-08-15","cited_by_count":0,"area":"agents"},{"id":"2608.17164","title":"SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version","created":"2026-08-17","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":"2608.24910","title":"From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction","created":"2026-07-22","cited_by_count":0,"area":"mllm"},{"id":"2609.07756","title":"Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction","created":"2026-09-07","cited_by_count":0,"area":"diffusion"},{"id":"2609.15393","title":"CodeTS: Verifiable Text-to-Time Series Generation via Executable Code","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.21425","title":"Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.24156","title":"TAC-Time: Texts as Channels For Multimodal Time Series Forecasting","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.38197","title":"DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.40265","title":"OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning","created":"2026-09-30","cited_by_count":0,"area":"lr"},{"id":"2610.04942","title":"Zero-Shot Time-Series Question Answering via Decoupled Perception and Reasoning","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.05211","title":"Image Synthesis as an Intermediate for Controllable Time Series Generation","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.05240","title":"Pythia: Toward Foundation World Models for Multimodal Time Series","created":"2026-10-04","cited_by_count":0,"area":"wm"},{"id":"2610.05686","title":"Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.05726","title":"FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.17794","title":"GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation","created":"2026-10-13","cited_by_count":0,"area":"agents"}],"next_cursor":null}