{"total":20,"rows":[{"id":"2412.08906","title":"Federated Foundation Models on Heterogeneous Time Series","created":"2024-12-12","cited_by_count":13,"area":null},{"id":"2505.02583","title":"Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era","created":"2025-05-05","cited_by_count":12,"area":null},{"id":"2510.09734","title":"ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting","created":"2025-10-10","cited_by_count":8,"area":null},{"id":"2508.12235","title":"CC-Time: Cross-Model and Cross-Modality Time Series Forecasting","created":"2025-08-17","cited_by_count":6,"area":null},{"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":"2411.15716","title":"Tackling Data Heterogeneity in Federated Time Series Forecasting","created":"2024-11-24","cited_by_count":3,"area":null},{"id":"2506.11040","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2508.01575","title":"KANMixer: a minimal KAN-centered mixer for long-term time series forecasting","created":"2025-08-03","cited_by_count":3,"area":null},{"id":"2504.02009","title":"Urban Computing in the Era of Large Language Models","created":"2025-04-02","cited_by_count":2,"area":null},{"id":"2509.26157","title":"Entropy Guided Dynamic Patch Segmentation for Time Series Transformers","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2601.12083","title":"Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models","created":"2026-01-17","cited_by_count":2,"area":null},{"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":"2503.07682","title":"A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2604.04475","title":"Discrete Prototypical Memories for Federated Time Series Foundation Models","created":"2026-04-06","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":"2508.00635","title":"KFS: KAN based adaptive Frequency Selection learning architecture for long term time series forecasting","created":"2025-08-01","cited_by_count":0,"area":null},{"id":"2510.05589","title":"Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2603.19899","title":"Deep Time-Series Forecasting in 10 Years: A Survey","created":"2026-03-20","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":"2607.13101","title":"TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling","created":"2026-07-14","cited_by_count":0,"area":null}],"next_cursor":null}