{"total":241,"rows":[{"id":"2310.01728","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","created":"2023-10-03","cited_by_count":700,"area":null},{"id":"2403.14735","title":"Foundation Models for Time Series Analysis: A Tutorial and Survey","created":"2024-03-21","cited_by_count":239,"area":null},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208,"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":"2407.13278","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","created":"2024-07-18","cited_by_count":197,"area":null},{"id":"2310.04948","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","created":"2023-10-08","cited_by_count":174,"area":null},{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131,"area":null},{"id":"2308.08469","title":"LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters","created":"2023-08-16","cited_by_count":129,"area":null},{"id":"2401.03955","title":"Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series","created":"2024-01-08","cited_by_count":127,"area":null},{"id":"2302.00861","title":"SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling","created":"2023-02-02","cited_by_count":115,"area":null},{"id":"2310.10196","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","created":"2023-10-16","cited_by_count":110,"area":null},{"id":"2306.10125","title":"Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects","created":"2023-06-16","cited_by_count":99,"area":null},{"id":"2403.00131","title":"UniTS: A Unified Multi-Task Time Series Model","created":"2024-02-29","cited_by_count":94,"area":null},{"id":"2410.20542","title":"PaPaGei: Open Foundation Models for Optical Physiological Signals","created":"2024-10-27","cited_by_count":69,"area":null},{"id":"2302.02515","title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","created":"2023-02-06","cited_by_count":68,"area":null},{"id":"2302.03133","title":"Domain Adaptation for Time Series Under Feature and Label Shifts","created":"2023-02-06","cited_by_count":60,"area":null},{"id":"2310.14017","title":"Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series","created":"2023-10-21","cited_by_count":59,"area":null},{"id":"2405.19363","title":"Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification","created":"2024-05-24","cited_by_count":53,"area":null},{"id":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","cited_by_count":52,"area":null},{"id":"2312.16424","title":"Soft Contrastive Learning for Time Series","created":"2023-12-27","cited_by_count":51,"area":null},{"id":"2312.01728","title":"ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation","created":"2023-12-04","cited_by_count":50,"area":null},{"id":"2402.17772","title":"EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs","created":"2024-02-17","cited_by_count":50,"area":null},{"id":"2506.09108","title":"SensorLM: Learning the Language of Wearable Sensors","created":"2025-06-10","cited_by_count":49,"area":null},{"id":"2303.00320","title":"TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders","created":"2023-03-01","cited_by_count":44,"area":null},{"id":"2306.02109","title":"Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency","created":"2023-06-03","cited_by_count":42,"area":null},{"id":"2302.02173","title":"A Survey on Deep Learning based Time Series Analysis with Frequency Transformation","created":"2023-02-04","cited_by_count":40,"area":null},{"id":"2208.06616","title":"Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification","created":"2022-08-13","cited_by_count":39,"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":"2502.01108","title":"Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings","created":"2025-02-03","cited_by_count":37,"area":null},{"id":"2312.16427","title":"Learning to Embed Time Series Patches Independently","created":"2023-12-27","cited_by_count":35,"area":null},{"id":"2310.20071","title":"FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space","created":"2023-10-30","cited_by_count":33,"area":null},{"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,"area":null},{"id":"2412.09758","title":"Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals","created":"2024-12-12","cited_by_count":32,"area":null},{"id":"2507.11783","title":"EEG Foundation Models: A Critical Review of Current Progress and Future Directions","created":"2025-07-15","cited_by_count":32,"area":null},{"id":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2310.03916","title":"Toward a Foundation Model for Time Series Data","created":"2023-10-05","cited_by_count":28,"area":null},{"id":"2405.14252","title":"Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting","created":"2024-05-23","cited_by_count":26,"area":null},{"id":"2410.19818","title":"UniMTS: Unified Pre-training for Motion Time Series","created":"2024-10-18","cited_by_count":26,"area":null},{"id":"2402.04852","title":"Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning","created":"2024-02-07","cited_by_count":25,"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":"2309.05927","title":"Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals","created":"2023-09-12","cited_by_count":23,"area":null},{"id":"2311.11413","title":"Large Pre-trained time series models for cross-domain Time series analysis tasks","created":"2023-11-19","cited_by_count":23,"area":null},{"id":"2506.05321","title":"LSM-2: Learning from Incomplete Wearable Sensor Data","created":"2025-06-05","cited_by_count":23,"area":null},{"id":"2503.00580","title":"Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery","created":"2025-03-01","cited_by_count":22,"area":null},{"id":"2311.00519","title":"REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning","created":"2023-11-01","cited_by_count":21,"area":null},{"id":"2305.10351","title":"BIOT: Cross-data Biosignal Learning in the Wild","created":"2023-05-10","cited_by_count":20,"area":null},{"id":"2310.07402","title":"NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining","created":"2023-10-11","cited_by_count":20,"area":null},{"id":"2402.02475","title":"TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling","created":"2024-02-04","cited_by_count":20,"area":null},{"id":"2411.18822","title":"RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data","created":"2024-11-27","cited_by_count":20,"area":null},{"id":"2401.05446","title":"Self-supervised Learning for Electroencephalogram: A Systematic Survey","created":"2024-01-09","cited_by_count":19,"area":null},{"id":"2403.05026","title":"Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts","created":"2024-03-08","cited_by_count":19,"area":null},{"id":"2410.15997","title":"MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast","created":"2024-10-21","cited_by_count":19,"area":null},{"id":"2309.13439","title":"Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning","created":"2023-09-23","cited_by_count":18,"area":null},{"id":"2403.01133","title":"Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data","created":"2024-03-02","cited_by_count":17,"area":null},{"id":"2403.05798","title":"$\\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting","created":"2024-03-09","cited_by_count":17,"area":null},{"id":"2409.00122","title":"Brant-X: A Unified Physiological Signal Alignment Framework","created":"2024-08-28","cited_by_count":17,"area":null},{"id":"2410.02131","title":"Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners","created":"2024-10-03","cited_by_count":17,"area":null},{"id":"2302.06433","title":"Label-efficient Time Series Representation Learning: A Review","created":"2023-02-13","cited_by_count":16,"area":null},{"id":"2307.10923","title":"Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series","created":"2023-07-20","cited_by_count":16,"area":null},{"id":"2410.04814","title":"Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data","created":"2024-10-07","cited_by_count":15,"area":null},{"id":"2509.25449","title":"Joint Embeddings Go Temporal","created":"2025-09-29","cited_by_count":14,"area":null},{"id":"2302.03357","title":"Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach","created":"2023-02-07","cited_by_count":13,"area":null},{"id":"2402.10434","title":"Parametric Augmentation for Time Series Contrastive Learning","created":"2024-02-16","cited_by_count":12,"area":null},{"id":"2505.13033","title":"TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis","created":"2025-05-19","cited_by_count":12,"area":null},{"id":"2309.05202","title":"Graph-Aware Contrasting for Multivariate Time-Series Classification","created":"2023-09-11","cited_by_count":11,"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":"2312.04142","title":"TimeDRL: Disentangled Representation Learning for Multivariate Time-Series","created":"2023-12-07","cited_by_count":10,"area":null},{"id":"2502.11965","title":"A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency","created":"2025-02-17","cited_by_count":10,"area":null},{"id":"2305.18888","title":"A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning","created":"2023-05-30","cited_by_count":9,"area":null},{"id":"2403.07245","title":"Dataset Condensation for Time Series Classification via Dual Domain Matching","created":"2024-03-12","cited_by_count":9,"area":null},{"id":"2403.11047","title":"From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting","created":"2024-03-17","cited_by_count":9,"area":null},{"id":"2408.04057","title":"PowerPM: Foundation Model for Power Systems","created":"2024-08-07","cited_by_count":9,"area":null},{"id":"2409.19130","title":"Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion","created":"2024-09-27","cited_by_count":9,"area":null},{"id":"2510.26777","title":"Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification","created":"2025-10-30","cited_by_count":9,"area":null},{"id":"2601.06134","title":"DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI","created":"2026-01-05","cited_by_count":9,"area":null},{"id":"2305.06472","title":"ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps","created":"2023-05-10","cited_by_count":8,"area":null},{"id":"2308.12551","title":"A Co-training Approach for Noisy Time Series Learning","created":"2023-08-24","cited_by_count":8,"area":null},{"id":"2312.03998","title":"Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification","created":"2023-12-07","cited_by_count":8,"area":null},{"id":"2405.10093","title":"LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting","created":"2024-05-16","cited_by_count":8,"area":null},{"id":"2410.07299","title":"OTIS: Learning High-Quality Time Series Features With Tiny Encoders","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2307.09614","title":"Multi-view self-supervised learning for multivariate variable-channel time series","created":"2023-07-13","cited_by_count":7,"area":null},{"id":"2309.15292","title":"Scaling Representation Learning from Ubiquitous ECG with State-Space Models","created":"2023-09-26","cited_by_count":7,"area":null},{"id":"2402.14041","title":"E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series","created":"2024-02-21","cited_by_count":7,"area":null},{"id":"2410.23625","title":"EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography","created":"2024-10-31","cited_by_count":7,"area":null},{"id":"2508.04251","title":"T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion","created":"2025-08-06","cited_by_count":7,"area":null},{"id":"2602.11558","title":"Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal","created":"2026-02-12","cited_by_count":7,"area":null},{"id":"2208.00877","title":"Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition","created":"2022-07-12","cited_by_count":6,"area":null},{"id":"2407.12415","title":"Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting","created":"2024-07-17","cited_by_count":6,"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":"2306.06579","title":"Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder","created":"2023-06-11","cited_by_count":5,"area":null},{"id":"2308.01011","title":"Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach","created":"2023-08-02","cited_by_count":5,"area":null},{"id":"2402.05960","title":"Phase-driven Domain Generalizable Learning for Nonstationary Time Series","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2403.12641","title":"Automated Contrastive Learning Strategy Search for Time Series","created":"2024-03-19","cited_by_count":5,"area":null},{"id":"2405.02766","title":"Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning","created":"2024-05-04","cited_by_count":5,"area":null},{"id":"2405.10597","title":"UniCL: A Universal Contrastive Learning Framework for Large Time Series Models","created":"2024-05-17","cited_by_count":5,"area":null},{"id":"2406.04627","title":"Denoising-Aware Contrastive Learning for Noisy Time Series","created":"2024-06-07","cited_by_count":5,"area":null},{"id":"2407.09336","title":"Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification","created":"2024-07-12","cited_by_count":5,"area":null},{"id":"2409.16554","title":"EMIT- Event-Based Masked Auto Encoding for Irregular Time Series","created":"2024-09-25","cited_by_count":5,"area":null},{"id":"2501.01342","title":"DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring","created":"2025-01-02","cited_by_count":5,"area":null},{"id":"2501.16368","title":"Foundation Models for CPS-IoT: Opportunities and Challenges","created":"2025-01-22","cited_by_count":5,"area":null}],"next_cursor":"100"}