{"total":138,"rows":[{"id":"2210.02186","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","created":"2022-10-05","cited_by_count":1058,"area":null},{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759,"area":null},{"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":"2302.11939","title":"One Fits All:Power General Time Series Analysis by Pretrained LM","created":"2023-02-23","cited_by_count":632,"area":null},{"id":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500,"area":null},{"id":"2205.14415","title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting","created":"2022-05-28","cited_by_count":391,"area":null},{"id":"2405.14616","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","created":"2024-05-23","cited_by_count":346,"area":null},{"id":"2403.20150","title":"TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods","created":"2024-03-29","cited_by_count":249,"area":null},{"id":"2307.03756","title":"FITS: Modeling Time Series with $10k$ Parameters","created":"2023-07-06","cited_by_count":195,"area":null},{"id":"2510.15821","title":"Chronos-2: From Univariate to Universal Forecasting","created":"2025-10-17","cited_by_count":194,"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":"2205.08897","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","created":"2022-05-18","cited_by_count":147,"area":null},{"id":"2410.16032","title":"TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis","created":"2024-10-21","cited_by_count":116,"area":null},{"id":"2305.18803","title":"Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors","created":"2023-05-30","cited_by_count":113,"area":null},{"id":"2403.07300","title":"CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning","created":"2024-03-12","cited_by_count":108,"area":null},{"id":"2203.10716","title":"Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices","created":"2022-03-21","cited_by_count":55,"area":null},{"id":"2305.12095","title":"CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting","created":"2023-05-20","cited_by_count":55,"area":null},{"id":"2206.04038","title":"Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting","created":"2022-06-08","cited_by_count":54,"area":null},{"id":"2302.04501","title":"MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing","created":"2023-02-09","cited_by_count":53,"area":null},{"id":"2411.01623","title":"FilterNet: Harnessing Frequency Filters for Time Series Forecasting","created":"2024-11-03","cited_by_count":47,"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":"2311.04147","title":"Multi-resolution Time-Series Transformer for Long-term Forecasting","created":"2023-11-07","cited_by_count":41,"area":null},{"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":"2303.08011","title":"Model scale versus domain knowledge in statistical forecasting of chaotic systems","created":"2023-03-13","cited_by_count":27,"area":null},{"id":"2403.01493","title":"ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis","created":"2024-03-03","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":"2411.02796","title":"Specialized Foundation Models Struggle to Beat Supervised Baselines","created":"2024-11-05","cited_by_count":24,"area":null},{"id":"2401.09093","title":"RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks","created":"2024-01-17","cited_by_count":22,"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":"2212.08151","title":"First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting","created":"2022-12-15","cited_by_count":19,"area":null},{"id":"2510.25785","title":"HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series","created":"2025-10-28","cited_by_count":19,"area":null},{"id":"2207.06046","title":"Learning Deep Time-index Models for Time Series Forecasting","created":"2022-07-13","cited_by_count":18,"area":null},{"id":"2202.07586","title":"Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection","created":"2022-02-15","cited_by_count":16,"area":null},{"id":"2212.01209","title":"FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting","created":"2022-12-02","cited_by_count":15,"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":"2412.08906","title":"Federated Foundation Models on Heterogeneous Time Series","created":"2024-12-12","cited_by_count":13,"area":null},{"id":"2502.15016","title":"TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation","created":"2025-02-20","cited_by_count":12,"area":null},{"id":"2410.05780","title":"GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks","created":"2024-10-08","cited_by_count":11,"area":null},{"id":"2502.21245","title":"TimesBERT: A BERT-Style Foundation Model for Time Series Understanding","created":"2025-02-28","cited_by_count":10,"area":null},{"id":"2508.04045","title":"FeDaL: Federated Dataset Learning for General Time Series Foundation Models","created":"2025-08-06","cited_by_count":10,"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":"2405.10995","title":"Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation","created":"2024-05-16","cited_by_count":8,"area":null},{"id":"2207.03517","title":"HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python","created":"2022-07-07","cited_by_count":7,"area":null},{"id":"2302.02077","title":"Cross-Frequency Time Series Meta-Forecasting","created":"2023-02-04","cited_by_count":5,"area":null},{"id":"2405.00319","title":"Data Augmentation Policy Search for Long-Term Forecasting","created":"2024-05-01","cited_by_count":5,"area":null},{"id":"2405.05499","title":"Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting","created":"2024-05-09","cited_by_count":5,"area":null},{"id":"2410.13792","title":"Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning","created":"2024-10-17","cited_by_count":5,"area":null},{"id":"2411.04554","title":"Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis","created":"2024-11-07","cited_by_count":5,"area":null},{"id":"2304.01512","title":"Handling Concept Drift in Global Time Series Forecasting","created":"2023-04-04","cited_by_count":4,"area":null},{"id":"2311.04770","title":"Vital Sign Forecasting for Sepsis Patients in ICUs","created":"2023-11-08","cited_by_count":4,"area":null},{"id":"2311.14782","title":"Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View","created":"2023-11-24","cited_by_count":4,"area":null},{"id":"2407.20503","title":"A federated large language model for long-term time series forecasting","created":"2024-07-30","cited_by_count":4,"area":null},{"id":"2502.10786","title":"Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak","created":"2025-02-15","cited_by_count":4,"area":null},{"id":"2504.12721","title":"TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations","created":"2025-04-17","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":"2308.00709","title":"DeepTSF: Codeless machine learning operations for time series forecasting","created":"2023-07-28","cited_by_count":3,"area":null},{"id":"2310.03243","title":"Sparse Deep Learning for Time Series Data: Theory and Applications","created":"2023-10-05","cited_by_count":3,"area":null},{"id":"2405.10877","title":"WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2408.11619","title":"Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation","created":"2024-08-21","cited_by_count":3,"area":null},{"id":"2509.13906","title":"TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates","created":"2025-09-17","cited_by_count":3,"area":null},{"id":"2301.09811","title":"Multi-view Kernel PCA for Time series Forecasting","created":"2023-01-24","cited_by_count":2,"area":null},{"id":"2302.00962","title":"FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting","created":"2023-02-02","cited_by_count":2,"area":null},{"id":"2307.06736","title":"MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2309.13135","title":"Global Deep Forecasting with Patient-Specific Pharmacokinetics","created":"2023-09-22","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":"2401.02465","title":"Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows","created":"2024-01-04","cited_by_count":2,"area":null},{"id":"2402.12539","title":"Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage","created":"2024-02-19","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":"2507.23253","title":"Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2601.11184","title":"TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation","created":"2026-01-16","cited_by_count":2,"area":null},{"id":"2210.12515","title":"SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data","created":"2022-10-22","cited_by_count":1,"area":null},{"id":"2210.17298","title":"A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformer","created":"2022-10-28","cited_by_count":1,"area":null},{"id":"2212.07771","title":"Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting","created":"2022-12-15","cited_by_count":1,"area":null},{"id":"2303.10426","title":"Discovering Predictable Latent Factors for Time Series Forecasting","created":"2023-03-18","cited_by_count":1,"area":null},{"id":"2305.04498","title":"Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings","created":"2023-05-08","cited_by_count":1,"area":null},{"id":"2305.18382","title":"Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers","created":"2023-05-28","cited_by_count":1,"area":null},{"id":"2308.11225","title":"On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report","created":"2023-08-22","cited_by_count":1,"area":null},{"id":"2308.13118","title":"Business Metric-Aware Forecasting for Inventory Management","created":"2023-08-24","cited_by_count":1,"area":null},{"id":"2309.15946","title":"Unified Long-Term Time-Series Forecasting Benchmark","created":"2023-09-27","cited_by_count":1,"area":null},{"id":"2312.15002","title":"C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2405.03429","title":"ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers","created":"2024-05-06","cited_by_count":1,"area":null},{"id":"2408.12408","title":"An Evaluation of Deep Learning Models for Stock Market Trend Prediction","created":"2024-08-22","cited_by_count":1,"area":null},{"id":"2410.11709","title":"GeOT: A spatially explicit framework for evaluating spatio-temporal predictions","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2503.20148","title":"Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques","created":"2025-03-26","cited_by_count":1,"area":null},{"id":"2504.17461","title":"Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience","created":"2025-04-24","cited_by_count":1,"area":null},{"id":"2505.18747","title":"Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2506.04677","title":"The cost of ensembling: is it always worth combining?","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2507.09445","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","created":"2025-07-13","cited_by_count":1,"area":null},{"id":"2507.12908","title":"Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2510.04386","title":"SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management","created":"2025-10-05","cited_by_count":1,"area":null},{"id":"2512.10229","title":"Adaptive Information Routing for Multimodal Time Series Forecasting","created":"2025-12-11","cited_by_count":1,"area":null},{"id":"2601.11350","title":"FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting","created":"2026-01-16","cited_by_count":1,"area":null},{"id":"2601.18837","title":"Time series forecasting with Hahn Kolmogorov-Arnold networks","created":"2026-01-25","cited_by_count":1,"area":null},{"id":"2605.03789","title":"Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2607.09400","title":"On-Device Adaptive Battery Power Prediction for Electric Vehicles","created":"2026-07-10","cited_by_count":1,"area":null},{"id":"2302.01800","title":"Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks","created":"2023-02-03","cited_by_count":0,"area":null},{"id":"2302.02597","title":"ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information","created":"2023-02-06","cited_by_count":0,"area":null},{"id":"2305.15196","title":"Feature-aligned N-BEATS with Sinkhorn divergence","created":"2023-05-24","cited_by_count":0,"area":null},{"id":"2306.05989","title":"QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting","created":"2023-06-09","cited_by_count":0,"area":null}],"next_cursor":"100"}