{"total":753,"rows":[{"id":"2106.13008","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","created":"2021-06-24","cited_by_count":1676,"area":null},{"id":"2201.12740","title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","created":"2022-01-30","cited_by_count":1210,"area":null},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"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":"2310.06625","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","created":"2023-10-10","cited_by_count":931,"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":"2310.10688","title":"A decoder-only foundation model for time-series forecasting","created":"2023-10-14","cited_by_count":692,"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":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614,"area":null},{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608,"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":"2402.03885","title":"MOMENT: A Family of Open Time-series Foundation Models","created":"2024-02-06","cited_by_count":482,"area":null},{"id":"2106.10466","title":"TS2Vec: Towards Universal Representation of Time Series","created":"2021-06-19","cited_by_count":471,"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":"2106.09305","title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","created":"2021-06-17","cited_by_count":338,"area":null},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337,"area":"diffusion"},{"id":"2301.10343","title":"ClimaX: A foundation model for weather and climate","created":"2023-01-24","cited_by_count":337,"area":null},{"id":"2304.08424","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","created":"2023-04-17","cited_by_count":332,"area":null},{"id":"2103.07719","title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting","created":"2021-03-13","cited_by_count":299,"area":null},{"id":"2409.16040","title":"Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts","created":"2024-09-24","cited_by_count":287,"area":"moe"},{"id":"2105.06643","title":"Monash Time Series Forecasting Archive","created":"2021-05-14","cited_by_count":267,"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":"2402.19072","title":"TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables","created":"2024-02-29","cited_by_count":248,"area":null},{"id":"2303.06053","title":"TSMixer: An All-MLP Architecture for Time Series Forecasting","created":"2023-03-10","cited_by_count":240,"area":null},{"id":"2410.10393","title":"GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation","created":"2024-10-14","cited_by_count":212,"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":"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":"2502.00816","title":"Sundial: A Family of Highly Capable Time Series Foundation Models","created":"2025-02-02","cited_by_count":180,"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":"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":"2207.01186","title":"Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures","created":"2022-07-04","cited_by_count":160,"area":null},{"id":"2310.03589","title":"TimeGPT-1","created":"2023-10-05","cited_by_count":158,"area":null},{"id":"2202.01381","title":"ETSformer: Exponential Smoothing Transformers for Time-series Forecasting","created":"2022-02-03","cited_by_count":148,"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":"2201.12886","title":"N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting","created":"2022-01-30","cited_by_count":146,"area":null},{"id":"2202.01575","title":"CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting","created":"2022-02-03","cited_by_count":144,"area":null},{"id":"2111.08095","title":"TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation","created":"2021-11-15","cited_by_count":142,"area":null},{"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,"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":"2206.09112","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","created":"2022-06-18","cited_by_count":123,"area":null},{"id":"2505.23719","title":"TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning","created":"2025-05-29","cited_by_count":120,"area":null},{"id":"2310.06119","title":"Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis","created":"2023-10-09","cited_by_count":117,"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":"2310.08278","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","created":"2023-10-12","cited_by_count":113,"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":"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":"2311.01933","title":"ForecastPFN: Synthetically-Trained Zero-Shot Forecasting","created":"2023-11-03","cited_by_count":104,"area":null},{"id":"2304.05206","title":"The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting","created":"2023-04-11","cited_by_count":101,"area":null},{"id":"2202.04770","title":"Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion","created":"2022-02-08","cited_by_count":96,"area":null},{"id":"2302.14829","title":"Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting","created":"2023-02-22","cited_by_count":93,"area":null},{"id":"2110.03224","title":"Darts: User-Friendly Modern Machine Learning for Time Series","created":"2021-10-07","cited_by_count":91,"area":null},{"id":"2110.05266","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","created":"2021-10-11","cited_by_count":88,"area":null},{"id":"2410.04803","title":"Timer-XL: Long-Context Transformers for Unified Time Series Forecasting","created":"2024-10-07","cited_by_count":87,"area":null},{"id":"2306.05043","title":"Non-autoregressive Conditional Diffusion Models for Time Series Prediction","created":"2023-06-08","cited_by_count":78,"area":"diffusion"},{"id":"2511.11698","title":"Moirai 2.0: When Less Is More for Time Series Forecasting","created":"2025-11-12","cited_by_count":69,"area":null},{"id":"2102.06828","title":"Domain Adaptation for Time Series Forecasting via Attention Sharing","created":"2021-02-13","cited_by_count":68,"area":null},{"id":"2402.02370","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","created":"2024-02-04","cited_by_count":65,"area":null},{"id":"2104.05522","title":"Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx","created":"2021-04-12","cited_by_count":64,"area":null},{"id":"2311.15599","title":"UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition","created":"2023-11-27","cited_by_count":64,"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":"2502.04395","title":"Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting","created":"2025-02-06","cited_by_count":63,"area":"mllm"},{"id":"2402.16132","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","created":"2024-02-25","cited_by_count":59,"area":null},{"id":"2108.00981","title":"PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series","created":"2021-08-02","cited_by_count":58,"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":"2303.09489","title":"Effectively Modeling Time Series with Simple Discrete State Spaces","created":"2023-03-16","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":"2303.11911","title":"Time Series Contrastive Learning with Information-Aware Augmentations","created":"2023-03-21","cited_by_count":52,"area":null},{"id":"2312.15709","title":"TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning","created":"2023-12-25","cited_by_count":52,"area":null},{"id":"2401.00423","title":"MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting","created":"2023-12-31","cited_by_count":50,"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":"2110.13716","title":"HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information","created":"2021-10-26","cited_by_count":45,"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":"2311.05709","title":"OmniVec: Learning robust representations with cross modal sharing","created":"2023-11-07","cited_by_count":44,"area":null},{"id":"2303.12783","title":"Conformal Prediction for Time Series with Modern Hopfield Networks","created":"2023-03-22","cited_by_count":42,"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":"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":"2402.17879","title":"Automated Statistical Model Discovery with Language Models","created":"2024-02-27","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":"2402.16412","title":"TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis","created":"2024-02-26","cited_by_count":39,"area":null},{"id":"2410.18712","title":"Retrieval-Augmented Diffusion Models for Time Series Forecasting","created":"2024-10-24","cited_by_count":39,"area":"rag"},{"id":"2501.03747","title":"Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series","created":"2025-01-07","cited_by_count":39,"area":null},{"id":"2102.10271","title":"Meta-Learning Dynamics Forecasting Using Task Inference","created":"2021-02-20","cited_by_count":38,"area":null},{"id":"2202.03528","title":"TACTiS: Transformer-Attentional Copulas for Time Series","created":"2022-02-07","cited_by_count":38,"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":"2404.01340","title":"From Similarity to Superiority: Channel Clustering for Time Series Forecasting","created":"2024-03-31","cited_by_count":37,"area":null},{"id":"2506.20093","title":"ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset","created":"2025-06-25","cited_by_count":37,"area":null},{"id":"2206.13816","title":"Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting","created":"2022-06-28","cited_by_count":36,"area":null},{"id":"2308.05566","title":"AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting","created":"2023-08-10","cited_by_count":36,"area":null},{"id":"2310.07446","title":"ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons","created":"2023-10-11","cited_by_count":36,"area":null},{"id":"2410.24087","title":"In-Context Fine-Tuning for Time-Series Foundation Models","created":"2024-10-31","cited_by_count":35,"area":null},{"id":"2205.04216","title":"Forecast combinations: an over 50-year review","created":"2022-05-09","cited_by_count":34,"area":null},{"id":"2406.03751","title":"Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting","created":"2024-06-06","cited_by_count":34,"area":null},{"id":"2401.13912","title":"A Survey of Deep Learning and Foundation Models for Time Series Forecasting","created":"2024-01-25","cited_by_count":33,"area":null},{"id":"2203.07681","title":"DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting","created":"2022-03-15","cited_by_count":29,"area":null}],"next_cursor":"100"}