{"total":1086,"rows":[{"id":"2211.14730","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","created":"2022-11-27","cited_by_count":2039,"area":null},{"id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","created":"2022-05-26","cited_by_count":1424,"area":null},{"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":"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":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500,"area":null},{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497,"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":"2205.14415","title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting","created":"2022-05-28","cited_by_count":391,"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":"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":"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":"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":"2210.08964","title":"PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting","created":"2022-09-20","cited_by_count":172,"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":"2406.16964","title":"Are Language Models Actually Useful for Time Series Forecasting?","created":"2024-06-22","cited_by_count":168,"area":null},{"id":"2310.03589","title":"TimeGPT-1","created":"2023-10-05","cited_by_count":158,"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":"2310.09751","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","created":"2023-10-15","cited_by_count":144,"area":null},{"id":"2306.09364","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","created":"2023-06-14","cited_by_count":139,"area":null},{"id":"2307.10802","title":"Meta-Transformer: A Unified Framework for Multimodal Learning","created":"2023-07-20","cited_by_count":136,"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":"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":"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":"2310.08278","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","created":"2023-10-12","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":"2405.00946","title":"SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters","created":"2024-05-02","cited_by_count":107,"area":null},{"id":"2409.18479","title":"CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","created":"2024-09-27","cited_by_count":106,"area":null},{"id":"2311.01933","title":"ForecastPFN: Synthetically-Trained Zero-Shot Forecasting","created":"2023-11-03","cited_by_count":104,"area":null},{"id":"2402.05956","title":"Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting","created":"2024-02-04","cited_by_count":104,"area":null},{"id":"2406.01638","title":"TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment","created":"2024-06-03","cited_by_count":103,"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":"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":"2406.11903","title":"A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges","created":"2024-06-15","cited_by_count":90,"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":"2303.12799","title":"Time Series as Images: Vision Transformer for Irregularly Sampled Time Series","created":"2023-03-01","cited_by_count":79,"area":null},{"id":"2402.02399","title":"FreDF: Learning to Forecast in the Frequency Domain","created":"2024-02-04","cited_by_count":79,"area":null},{"id":"2404.14197","title":"SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion","created":"2024-04-22","cited_by_count":76,"area":null},{"id":"2309.12659","title":"OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling","created":"2023-09-22","cited_by_count":74,"area":null},{"id":"2404.08472","title":"TSLANet: Rethinking Transformers for Time Series Representation Learning","created":"2024-04-12","cited_by_count":74,"area":null},{"id":"2406.08627","title":"Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis","created":"2024-06-12","cited_by_count":74,"area":null},{"id":"2211.15979","title":"AirFormer: Predicting Nationwide Air Quality in China with Transformers","created":"2022-11-29","cited_by_count":73,"area":null},{"id":"2401.06866","title":"Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data","created":"2024-01-12","cited_by_count":66,"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":"2301.08871","title":"Ti-MAE: Self-Supervised Masked Time Series Autoencoders","created":"2023-01-21","cited_by_count":63,"area":null},{"id":"2402.10635","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","created":"2024-02-16","cited_by_count":63,"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":"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":"2410.11802","title":"TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting","created":"2024-10-15","cited_by_count":58,"area":null},{"id":"2502.10721","title":"A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective","created":"2025-02-15","cited_by_count":58,"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":"2306.09368","title":"Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series","created":"2023-06-14","cited_by_count":55,"area":null},{"id":"2308.11200","title":"SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting","created":"2023-08-22","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":"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":"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":"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":"2308.03274","title":"DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction","created":"2023-08-07","cited_by_count":49,"area":null},{"id":"2210.09693","title":"TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis","created":"2022-10-18","cited_by_count":48,"area":null},{"id":"2407.07874","title":"Toto: Time Series Optimized Transformer for Observability","created":"2024-07-10","cited_by_count":48,"area":null},{"id":"2409.20371","title":"Frequency Adaptive Normalization For Non-stationary Time Series Forecasting","created":"2024-09-30","cited_by_count":47,"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":"2205.01138","title":"Transformers in Time-series Analysis: A Tutorial","created":"2022-04-28","cited_by_count":46,"area":null},{"id":"2510.23672","title":"DBLoss: Decomposition-based Loss Function for Time Series Forecasting","created":"2025-10-27","cited_by_count":45,"area":null},{"id":"2402.10198","title":"SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention","created":"2024-02-15","cited_by_count":44,"area":null},{"id":"2402.12694","title":"Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling","created":"2024-02-20","cited_by_count":44,"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":"2301.09152","title":"Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data","created":"2023-01-22","cited_by_count":40,"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":"2410.04442","title":"TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting","created":"2024-10-06","cited_by_count":40,"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":"2501.03747","title":"Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series","created":"2025-01-07","cited_by_count":39,"area":null},{"id":"2510.14510","title":"Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective","created":"2025-10-16","cited_by_count":39,"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":"2412.17323","title":"xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition","created":"2024-12-23","cited_by_count":36,"area":null},{"id":"2312.16427","title":"Learning to Embed Time Series Patches Independently","created":"2023-12-27","cited_by_count":35,"area":null},{"id":"2312.17346","title":"STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction","created":"2023-12-28","cited_by_count":35,"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":"2503.00877","title":"Patch-wise Structural Loss for Time Series Forecasting","created":"2025-03-02","cited_by_count":35,"area":null},{"id":"2312.03014","title":"Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey","created":"2023-12-05","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":"2405.15124","title":"Scaling Law for Time Series Forecasting","created":"2024-05-24","cited_by_count":33,"area":null}],"next_cursor":"100"}