{"list":"other","total":74,"rows":[{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2101.00190","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","created":"2021-01-01","cited_by_count":3915},{"id":"2309.17453","title":"Efficient Streaming Language Models with Attention Sinks","created":"2023-09-29","cited_by_count":2186},{"id":"2012.07436","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","created":"2020-12-14","cited_by_count":2169},{"id":"2211.14730","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","created":"2022-11-27","cited_by_count":2039},{"id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","created":"2022-05-26","cited_by_count":1424},{"id":"2201.12740","title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","created":"2022-01-30","cited_by_count":1210},{"id":"1709.04875","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","created":"2017-09-14","cited_by_count":1136},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759},{"id":"2404.06395","title":"MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies","created":"2024-04-09","cited_by_count":723},{"id":"2310.10688","title":"A decoder-only foundation model for time-series forecasting","created":"2023-10-14","cited_by_count":692},{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614},{"id":"2007.02842","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","created":"2020-07-06","cited_by_count":530},{"id":"1811.05320","title":"T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction","created":"2018-11-12","cited_by_count":401},{"id":"2405.14616","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","created":"2024-05-23","cited_by_count":346},{"id":"1610.00081","title":"Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction","created":"2016-10-01","cited_by_count":333},{"id":"2505.06708","title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","created":"2025-05-10","cited_by_count":283},{"id":"2402.19072","title":"TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables","created":"2024-02-29","cited_by_count":248},{"id":"1711.11053","title":"A Multi-Horizon Quantile Recurrent Forecaster","created":"2017-11-29","cited_by_count":211},{"id":"2001.02908","title":"Spatial-Temporal Transformer Networks for Traffic Flow Forecasting","created":"2020-01-09","cited_by_count":180},{"id":"1906.04928","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","created":"2019-06-11","cited_by_count":172},{"id":"2101.06861","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","created":"2021-01-18","cited_by_count":171},{"id":"2208.05233","title":"Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting","created":"2022-08-10","cited_by_count":151},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145},{"id":"2310.09751","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","created":"2023-10-15","cited_by_count":144},{"id":"2303.14483","title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey","created":"2023-03-25","cited_by_count":140},{"id":"2206.09112","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","created":"2022-06-18","cited_by_count":123},{"id":"2206.09113","title":"Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting","created":"2022-06-18","cited_by_count":117},{"id":"2410.06205","title":"Round and Round We Go! What makes Rotary Positional Encodings useful?","created":"2024-10-08","cited_by_count":115},{"id":"2402.11838","title":"UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction","created":"2024-02-19","cited_by_count":87},{"id":"2401.10134","title":"Spatial-Temporal Large Language Model for Traffic Prediction","created":"2024-01-18","cited_by_count":83},{"id":"2403.00813","title":"UrbanGPT: Spatio-Temporal Large Language Models","created":"2024-02-25","cited_by_count":81},{"id":"2410.05192","title":"Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective","created":"2024-10-07","cited_by_count":81},{"id":"2212.04475","title":"Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction","created":"2022-12-07","cited_by_count":77},{"id":"2211.15979","title":"AirFormer: Predicting Nationwide Air Quality in China with Transformers","created":"2022-11-29","cited_by_count":73},{"id":"2101.10025","title":"A Review of Graph Neural Networks and Their Applications in Power Systems","created":"2021-01-25","cited_by_count":63},{"id":"2502.04395","title":"Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting","created":"2025-02-06","cited_by_count":63},{"id":"2402.04059","title":"Deep Learning for Multivariate Time Series Imputation: A Survey","created":"2024-02-06","cited_by_count":57},{"id":"2203.01474","title":"Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction","created":"2022-03-03","cited_by_count":48},{"id":"2108.11873","title":"When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?","created":"2021-08-26","cited_by_count":36},{"id":"2008.08903","title":"Generative Adversarial Networks for Spatio-temporal Data: A Survey","created":"2020-08-18","cited_by_count":35},{"id":"2405.17478","title":"Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer","created":"2024-05-24","cited_by_count":32},{"id":"2406.13945","title":"CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks","created":"2024-06-20","cited_by_count":27},{"id":"2408.10269","title":"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction","created":"2024-08-16","cited_by_count":27},{"id":"2405.14252","title":"Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting","created":"2024-05-23","cited_by_count":26},{"id":"2410.19892","title":"Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems","created":"2024-10-25","cited_by_count":26},{"id":"2503.13502","title":"Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey","created":"2025-03-12","cited_by_count":24},{"id":"2301.10569","title":"Spatio-Temporal Graph Neural Networks: A Survey","created":"2023-01-25","cited_by_count":23},{"id":"2301.12603","title":"Do We Really Need Graph Neural Networks for Traffic Forecasting?","created":"2023-01-30","cited_by_count":20},{"id":"2207.05064","title":"Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting","created":"2022-07-09","cited_by_count":17},{"id":"2505.17871","title":"BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models","created":"2025-05-23","cited_by_count":16},{"id":"2408.12116","title":"Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning","created":"2024-08-22","cited_by_count":15},{"id":"2403.02354","title":"Spatio-Temporal Field Neural Networks for Air Quality Inference","created":"2024-03-02","cited_by_count":12},{"id":"2401.04148","title":"Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting","created":"2024-01-08","cited_by_count":11},{"id":"2405.06419","title":"Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting","created":"2024-05-10","cited_by_count":11},{"id":"2506.01364","title":"Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review","created":"2025-06-02","cited_by_count":10},{"id":"2103.09883","title":"A Survey on Spatio-temporal Data Analytics Systems","created":"2021-03-17","cited_by_count":9},{"id":"2506.00635","title":"Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting","created":"2025-05-31","cited_by_count":9},{"id":"2506.11109","title":"Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization","created":"2025-06-08","cited_by_count":8},{"id":"2302.08658","title":"Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting","created":"2023-02-17","cited_by_count":7},{"id":"2501.09045","title":"Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities","created":"2025-01-15","cited_by_count":6},{"id":"2506.15507","title":"Over-squashing in Spatiotemporal Graph Neural Networks","created":"2025-06-18","cited_by_count":6},{"id":"2302.11774","title":"Semantic-Fused Multi-Granularity Cross-City Traffic Prediction","created":"2023-02-23","cited_by_count":4},{"id":"2408.10822","title":"Navigating Spatio-Temporal Heterogeneity: A Graph Transformer Approach for Traffic Forecasting","created":"2024-08-20","cited_by_count":3},{"id":"2411.09251","title":"Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting","created":"2024-11-14","cited_by_count":3},{"id":"2411.14046","title":"REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting","created":"2024-11-21","cited_by_count":3},{"id":"2412.10912","title":"ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data","created":"2024-12-14","cited_by_count":3},{"id":"2501.13141","title":"AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks","created":"2025-01-22","cited_by_count":2},{"id":"2502.15296","title":"Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning","created":"2025-02-21","cited_by_count":2},{"id":"2506.03155","title":"Fusing Cross-Domain Knowledge from Multimodal Data to Solve Problems in the Physical World","created":"2025-05-20","cited_by_count":2},{"id":"2506.13678","title":"A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction","created":"2025-06-16","cited_by_count":2}],"next_cursor":null}