{"total":1260,"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":"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":"2405.14616","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","created":"2024-05-23","cited_by_count":346,"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":"2303.06053","title":"TSMixer: An All-MLP Architecture for Time Series Forecasting","created":"2023-03-10","cited_by_count":240,"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":"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":"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":"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":"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":"2306.09364","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","created":"2023-06-14","cited_by_count":139,"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":"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":"2310.10196","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":"2405.00946","title":"SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters","created":"2024-05-02","cited_by_count":107,"area":null},{"id":"2412.10859","title":"DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting","created":"2024-12-14","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":"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":"2403.00131","title":"UniTS: A Unified Multi-Task Time Series Model","created":"2024-02-29","cited_by_count":94,"area":null},{"id":"2410.12261","title":"CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching","created":"2024-10-16","cited_by_count":94,"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":"2410.04803","title":"Timer-XL: Long-Context Transformers for Unified Time Series Forecasting","created":"2024-10-07","cited_by_count":87,"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":"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":"2402.02370","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","created":"2024-02-04","cited_by_count":65,"area":null},{"id":"2509.26468","title":"fev-bench: A Realistic Benchmark for Time Series Forecasting","created":"2025-09-30","cited_by_count":64,"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":"2506.18046","title":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods","created":"2025-06-22","cited_by_count":60,"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":"2305.12095","title":"CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting","created":"2023-05-20","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":"2505.11250","title":"Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline","created":"2025-05-16","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":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","cited_by_count":52,"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":"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":"2310.02619","title":"Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs","created":"2023-10-04","cited_by_count":49,"area":null},{"id":"2407.07874","title":"Toto: Time Series Optimized Transformer for Observability","created":"2024-07-10","cited_by_count":48,"area":null},{"id":"2409.14327","title":"Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining","created":"2024-09-22","cited_by_count":47,"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":"2406.09009","title":"Fredformer: Frequency Debiased Transformer for Time Series Forecasting","created":"2024-06-13","cited_by_count":45,"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":"2403.14587","title":"An Analysis of Linear Time Series Forecasting Models","created":"2024-03-21","cited_by_count":41,"area":null},{"id":"2410.05440","title":"Can LLMs Understand Time Series Anomalies?","created":"2024-10-07","cited_by_count":41,"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":"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":"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":"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":"2405.15124","title":"Scaling Law for Time Series Forecasting","created":"2024-05-24","cited_by_count":33,"area":null},{"id":"2401.17548","title":"Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators","created":"2024-01-31","cited_by_count":32,"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":"2505.12917","title":"Temporal Query Network for Efficient Multivariate Time Series Forecasting","created":"2025-05-19","cited_by_count":31,"area":null},{"id":"2502.06910","title":"TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting","created":"2025-02-10","cited_by_count":30,"area":null},{"id":"2312.06786","title":"Mixture-of-Linear-Experts for Long-term Time Series Forecasting","created":"2023-12-11","cited_by_count":29,"area":null},{"id":"2502.16294","title":"TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data","created":"2025-02-22","cited_by_count":29,"area":null},{"id":"2509.22295","title":"Aurora: Towards Universal Generative Multimodal Time Series Forecasting","created":"2025-09-26","cited_by_count":29,"area":null},{"id":"2305.18475","title":"Approximation Rate of the Transformer Architecture for Sequence Modeling","created":"2023-05-29","cited_by_count":28,"area":null},{"id":"2407.09434","title":"Foundation Models for the Electric Power Grid","created":"2024-07-12","cited_by_count":28,"area":null},{"id":"2409.15771","title":"Zero-shot forecasting of chaotic systems","created":"2024-09-24","cited_by_count":28,"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":"2307.00142","title":"BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting","created":"2023-06-30","cited_by_count":26,"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":"2405.18975","title":"Hierarchical Classification Auxiliary Network for Time Series Forecasting","created":"2024-05-29","cited_by_count":26,"area":null}],"next_cursor":"100"}