{"total":138,"rows":[{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614,"area":null},{"id":"2206.08496","title":"Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency","created":"2022-06-17","cited_by_count":250,"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":"2311.06184","title":"Frequency-domain MLPs are More Effective Learners in Time Series Forecasting","created":"2023-11-10","cited_by_count":172,"area":null},{"id":"2308.08241","title":"TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series","created":"2023-08-16","cited_by_count":169,"area":null},{"id":"2311.06190","title":"FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective","created":"2023-11-10","cited_by_count":127,"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":"2310.08278","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","created":"2023-10-12","cited_by_count":113,"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":"2408.17253","title":"VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters","created":"2024-08-30","cited_by_count":88,"area":null},{"id":"2301.08871","title":"Ti-MAE: Self-Supervised Masked Time Series Autoencoders","created":"2023-01-21","cited_by_count":63,"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":"2410.14752","title":"TimeSeriesExam: A time series understanding exam","created":"2024-10-18","cited_by_count":49,"area":null},{"id":"2309.13378","title":"Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment","created":"2023-09-23","cited_by_count":47,"area":null},{"id":"2202.11672","title":"Learning Fast and Slow for Online Time Series Forecasting","created":"2022-02-23","cited_by_count":45,"area":null},{"id":"2310.20496","title":"BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis","created":"2023-10-31","cited_by_count":41,"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":"2210.03675","title":"Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts","created":"2022-10-07","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":"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":"2404.00225","title":"Heterogeneous Contrastive Learning for Foundation Models and Beyond","created":"2024-03-30","cited_by_count":27,"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":"2310.05063","title":"Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain","created":"2023-10-08","cited_by_count":23,"area":null},{"id":"2402.01749","title":"Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models","created":"2024-01-30","cited_by_count":23,"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":"2402.02475","title":"TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling","created":"2024-02-04","cited_by_count":20,"area":null},{"id":"2506.12459","title":"Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates","created":"2025-06-14","cited_by_count":20,"area":null},{"id":"2305.13036","title":"Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting","created":"2023-05-22","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":"2305.14582","title":"Interpretation of Time-Series Deep Models: A Survey","created":"2023-05-23","cited_by_count":15,"area":null},{"id":"2402.16516","title":"Generative Pretrained Hierarchical Transformer for Time Series Forecasting","created":"2024-02-26","cited_by_count":15,"area":null},{"id":"2204.12440","title":"Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining","created":"2022-04-20","cited_by_count":14,"area":null},{"id":"2308.01578","title":"Unsupervised Representation Learning for Time Series: A Review","created":"2023-08-03","cited_by_count":14,"area":null},{"id":"2410.05711","title":"TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation","created":"2024-10-08","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":"2305.10721","title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","created":"2023-05-18","cited_by_count":11,"area":null},{"id":"2401.04148","title":"Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting","created":"2024-01-08","cited_by_count":11,"area":null},{"id":"2308.14906","title":"BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition","created":"2023-08-28","cited_by_count":10,"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":"2402.11816","title":"Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning","created":"2024-02-19","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":"2307.08302","title":"GBT: Two-stage transformer framework for non-stationary time series forecasting","created":"2023-07-17","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":"2402.02023","title":"Self-Supervised Contrastive Learning for Long-term Forecasting","created":"2024-02-03","cited_by_count":8,"area":null},{"id":"2404.03702","title":"Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach","created":"2024-04-04","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":"2303.18205","title":"Simple Contrastive Representation Learning for Time Series Forecasting","created":"2023-03-31","cited_by_count":7,"area":null},{"id":"2412.00108","title":"Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data","created":"2024-11-28","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":"2305.00706","title":"Full Scaling Automation for Sustainable Development of Green Data Centers","created":"2023-05-01","cited_by_count":5,"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":"2402.01999","title":"A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge","created":"2024-02-03","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":"2412.15315","title":"Enhancing Masked Time-Series Modeling via Dropping Patches","created":"2024-12-19","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":"2205.15875","title":"SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2411.15743","title":"Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting","created":"2024-11-24","cited_by_count":4,"area":null},{"id":"2503.09712","title":"Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain","created":"2025-03-12","cited_by_count":4,"area":null},{"id":"2506.22393","title":"Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis","created":"2025-06-27","cited_by_count":4,"area":null},{"id":"2207.10941","title":"Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect","created":"2022-07-22","cited_by_count":3,"area":null},{"id":"2306.10703","title":"FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting","created":"2023-06-19","cited_by_count":3,"area":null},{"id":"2312.01699","title":"Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2405.18036","title":"ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks","created":"2024-05-28","cited_by_count":3,"area":null},{"id":"2410.01860","title":"FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecasting","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2410.04703","title":"Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2410.11200","title":"SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning","created":"2024-10-15","cited_by_count":3,"area":null},{"id":"2410.12866","title":"Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings","created":"2024-10-13","cited_by_count":3,"area":null},{"id":"2410.22981","title":"DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting","created":"2024-10-30","cited_by_count":3,"area":null},{"id":"2502.21086","title":"Are foundation models useful feature extractors for electroencephalography analysis?","created":"2025-02-28","cited_by_count":3,"area":null},{"id":"2608.12944","title":"CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation","created":"2026-08-13","cited_by_count":3,"area":null},{"id":"2306.12086","title":"What Constitutes Good Contrastive Learning in Time-Series Forecasting?","created":"2023-06-21","cited_by_count":2,"area":null},{"id":"2311.00886","title":"COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning","created":"2023-11-01","cited_by_count":2,"area":null},{"id":"2311.03768","title":"PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning","created":"2023-11-07","cited_by_count":2,"area":null},{"id":"2401.03341","title":"Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection","created":"2024-01-07","cited_by_count":2,"area":null},{"id":"2401.17802","title":"Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning","created":"2024-01-31","cited_by_count":2,"area":null},{"id":"2403.17814","title":"D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting","created":"2024-03-26","cited_by_count":2,"area":null},{"id":"2410.12606","title":"Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2504.16432","title":"iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network","created":"2025-04-23","cited_by_count":2,"area":null},{"id":"2509.00765","title":"Decomposing and Revising What Language Models Generate","created":"2025-08-31","cited_by_count":2,"area":null},{"id":"2602.04369","title":"Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis","created":"2026-02-04","cited_by_count":2,"area":null},{"id":"2602.05389","title":"A Decomposition-based State Space Model for Multivariate Time-Series Forecasting","created":"2026-02-05","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":"2605.11130","title":"HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2307.13209","title":"Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG","created":"2023-07-25","cited_by_count":1,"area":null},{"id":"2401.18057","title":"Rank Supervised Contrastive Learning for Time Series Classification","created":"2024-01-31","cited_by_count":1,"area":null},{"id":"2404.16886","title":"Review of Data-centric Time Series Analysis from Sample, Feature, and Period","created":"2024-04-24","cited_by_count":1,"area":null},{"id":"2410.08421","title":"Generalizable autoregressive modeling of time series through functional narratives","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2412.05534","title":"Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts","created":"2024-12-07","cited_by_count":1,"area":null},{"id":"2502.15785","title":"Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling","created":"2025-02-18","cited_by_count":1,"area":null},{"id":"2503.08328","title":"MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2504.18878","title":"TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation","created":"2025-04-26","cited_by_count":1,"area":null},{"id":"2505.16308","title":"Beyond All-to-All: Causal-Aligned Transformer with Dynamic Structure Learning for Multivariate Time Series Forecasting","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2505.20480","title":"BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2506.06310","title":"Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2507.00013","title":"ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting","created":"2025-06-13","cited_by_count":1,"area":null}],"next_cursor":"100"}