{"total":416,"rows":[{"id":"2106.10466","title":"TS2Vec: Towards Universal Representation of Time Series","created":"2021-06-19","cited_by_count":471,"area":null},{"id":"2105.06643","title":"Monash Time Series Forecasting Archive","created":"2021-05-14","cited_by_count":267,"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":"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":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"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":"2104.07551","title":"HIVE-COTE 2.0: a new meta ensemble for time series classification","created":"2021-04-15","cited_by_count":118,"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":"2306.10347","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection","created":"2023-06-17","cited_by_count":103,"area":null},{"id":"2304.13029","title":"Bake off redux: a review and experimental evaluation of recent time series classification algorithms","created":"2023-04-25","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":"2106.09296","title":"Voice2Series: Reprogramming Acoustic Models for Time Series Classification","created":"2021-06-17","cited_by_count":91,"area":null},{"id":"2102.00457","title":"MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification","created":"2021-01-31","cited_by_count":90,"area":null},{"id":"2301.03028","title":"Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement","created":"2023-01-08","cited_by_count":90,"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":"2112.15424","title":"A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges","created":"2021-11-12","cited_by_count":69,"area":null},{"id":"2302.02515","title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","created":"2023-02-06","cited_by_count":68,"area":null},{"id":"2202.10629","title":"Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning","created":"2022-02-22","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":"2506.18046","title":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods","created":"2025-06-22","cited_by_count":60,"area":null},{"id":"2510.15870","title":"OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM","created":"2025-10-17","cited_by_count":59,"area":null},{"id":"2111.00418","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","created":"2021-10-31","cited_by_count":54,"area":null},{"id":"2203.09270","title":"Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series","created":"2022-03-17","cited_by_count":54,"area":null},{"id":"2203.13652","title":"HYDRA: Competing convolutional kernels for fast and accurate time series classification","created":"2022-03-25","cited_by_count":54,"area":null},{"id":"2202.05656","title":"Evaluation of post-hoc interpretability methods in time-series classification","created":"2022-02-11","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":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","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":"2312.16424","title":"Soft Contrastive Learning for Time Series","created":"2023-12-27","cited_by_count":51,"area":null},{"id":"2302.12721","title":"LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version","created":"2023-02-24","cited_by_count":50,"area":null},{"id":"2402.14989","title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","created":"2024-02-22","cited_by_count":36,"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":"2405.09308","title":"TimeX++: Learning Time-Series Explanations with Information Bottleneck","created":"2024-05-15","cited_by_count":33,"area":null},{"id":"2410.12360","title":"Towards Neural Scaling Laws for Time Series Foundation Models","created":"2024-10-16","cited_by_count":33,"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":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2305.11921","title":"An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set","created":"2023-05-19","cited_by_count":29,"area":null},{"id":"2506.06005","title":"LightGTS: A Lightweight General Time Series Forecasting Model","created":"2025-06-06","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":"2310.03916","title":"Toward a Foundation Model for Time Series Data","created":"2023-10-05","cited_by_count":28,"area":null},{"id":"2105.03841","title":"The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification","created":"2021-05-09","cited_by_count":26,"area":null},{"id":"2309.03755","title":"TSGBench: Time Series Generation Benchmark","created":"2023-09-07","cited_by_count":26,"area":null},{"id":"2204.03342","title":"Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift","created":"2022-04-07","cited_by_count":24,"area":null},{"id":"2209.06528","title":"Explainable AI for clinical and remote health applications: a survey on tabular and time series data","created":"2022-09-14","cited_by_count":24,"area":null},{"id":"2502.15637","title":"Mantis: Lightweight Foundation Model for Time Series Classification","created":"2025-02-21","cited_by_count":24,"area":null},{"id":"2103.09458","title":"Learning Discriminative Prototypes with Dynamic Time Warping","created":"2021-03-17","cited_by_count":23,"area":null},{"id":"2311.10049","title":"Inherently Interpretable Time Series Classification via Multiple Instance Learning","created":"2023-11-16","cited_by_count":23,"area":null},{"id":"2405.02358","title":"Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey","created":"2024-05-03","cited_by_count":23,"area":null},{"id":"2109.13514","title":"Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets","created":"2021-09-28","cited_by_count":22,"area":null},{"id":"2212.09840","title":"Dynamic Sparse Network for Time Series Classification: Learning What to \"see''","created":"2022-12-19","cited_by_count":22,"area":null},{"id":"2312.17004","title":"Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects","created":"2023-12-28","cited_by_count":22,"area":null},{"id":"2207.13987","title":"ClaSP -- Parameter-free Time Series Segmentation","created":"2022-07-28","cited_by_count":21,"area":null},{"id":"2310.07402","title":"NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining","created":"2023-10-11","cited_by_count":20,"area":null},{"id":"2205.07649","title":"Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder","created":"2022-05-16","cited_by_count":19,"area":null},{"id":"2310.04486","title":"T-Rep: Representation Learning for Time Series using Time-Embeddings","created":"2023-10-06","cited_by_count":19,"area":null},{"id":"2201.12048","title":"The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier","created":"2022-01-28","cited_by_count":18,"area":null},{"id":"2206.03735","title":"Motiflets -- Simple and Accurate Detection of Motifs in Time Series","created":"2022-06-08","cited_by_count":16,"area":null},{"id":"2408.07314","title":"Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis","created":"2024-08-14","cited_by_count":16,"area":null},{"id":"2509.22284","title":"Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models","created":"2025-09-26","cited_by_count":16,"area":null},{"id":"2301.10194","title":"WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification","created":"2023-01-24","cited_by_count":15,"area":null},{"id":"2306.03801","title":"Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures","created":"2023-06-06","cited_by_count":15,"area":null},{"id":"2308.00928","title":"QUANT: A Minimalist Interval Method for Time Series Classification","created":"2023-08-02","cited_by_count":15,"area":null},{"id":"2201.00011","title":"An Efficient Federated Distillation Learning System for Multi-task Time Series Classification","created":"2021-12-30","cited_by_count":14,"area":null},{"id":"2202.08055","title":"HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing","created":"2022-02-16","cited_by_count":14,"area":null},{"id":"2205.15181","title":"A Review and Evaluation of Elastic Distance Functions for Time Series Clustering","created":"2022-05-30","cited_by_count":14,"area":null},{"id":"2502.12370","title":"Positional Encoding in Transformer-Based Time Series Models: A Survey","created":"2025-02-17","cited_by_count":14,"area":null},{"id":"2505.02874","title":"Uncertainty Quantification for Machine Learning in Healthcare: A Survey","created":"2025-05-04","cited_by_count":14,"area":null},{"id":"2505.20446","title":"Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach","created":"2025-05-26","cited_by_count":14,"area":null},{"id":"2509.25449","title":"Joint Embeddings Go Temporal","created":"2025-09-29","cited_by_count":14,"area":null},{"id":"2201.01413","title":"Towards Similarity-Aware Time-Series Classification","created":"2022-01-05","cited_by_count":13,"area":null},{"id":"2201.02143","title":"Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks","created":"2022-01-06","cited_by_count":13,"area":null},{"id":"2301.12495","title":"Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology","created":"2023-01-29","cited_by_count":13,"area":null},{"id":"2410.05629","title":"Vector-ICL: In-context Learning with Continuous Vector Representations","created":"2024-10-08","cited_by_count":13,"area":null},{"id":"2211.02566","title":"scikit-fda: A Python Package for Functional Data Analysis","created":"2022-11-04","cited_by_count":12,"area":null},{"id":"2211.04411","title":"Motif-guided Time Series Counterfactual Explanations","created":"2022-11-08","cited_by_count":12,"area":null},{"id":"2402.10434","title":"Parametric Augmentation for Time Series Contrastive Learning","created":"2024-02-16","cited_by_count":12,"area":null},{"id":"2110.06324","title":"A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids","created":"2021-10-12","cited_by_count":11,"area":null},{"id":"2207.12165","title":"dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification","created":"2022-07-25","cited_by_count":11,"area":null},{"id":"2302.03284","title":"Unsupervised Deep Learning for IoT Time Series","created":"2023-02-07","cited_by_count":11,"area":null},{"id":"2307.05104","title":"A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI","created":"2023-07-11","cited_by_count":11,"area":null},{"id":"2601.23204","title":"TSAQA: Time Series Analysis Question And Answering Benchmark","created":"2026-01-30","cited_by_count":11,"area":null},{"id":"2602.17868","title":"MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies","created":"2026-02-19","cited_by_count":11,"area":null},{"id":"2410.24100","title":"Benchmark Data Repositories for Better Benchmarking","created":"2024-10-31","cited_by_count":10,"area":null},{"id":"2411.12274","title":"A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data","created":"2024-11-19","cited_by_count":10,"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":"2505.06892","title":"Learning Soft Sparse Shapes for Efficient Time-Series Classification","created":"2025-05-11","cited_by_count":10,"area":null},{"id":"2506.05035","title":"TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation","created":"2025-06-05","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":"2209.06388","title":"TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack","created":"2022-09-14","cited_by_count":9,"area":null},{"id":"2310.10060","title":"Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey","created":"2023-10-16","cited_by_count":9,"area":null},{"id":"2311.12214","title":"Random Fourier Signature Features","created":"2023-11-20","cited_by_count":9,"area":null},{"id":"2403.07245","title":"Dataset Condensation for Time Series Classification via Dual Domain Matching","created":"2024-03-12","cited_by_count":9,"area":null},{"id":"2409.02869","title":"Look Into the LITE in Deep Learning for Time Series Classification","created":"2024-09-04","cited_by_count":9,"area":null},{"id":"2509.25826","title":"Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models","created":"2025-09-30","cited_by_count":9,"area":null},{"id":"2209.09114","title":"Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description","created":"2022-09-15","cited_by_count":8,"area":null},{"id":"2210.15050","title":"TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting","created":"2022-10-26","cited_by_count":8,"area":null},{"id":"2301.11535","title":"Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective","created":"2023-01-27","cited_by_count":8,"area":null},{"id":"2309.14518","title":"Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels","created":"2023-09-25","cited_by_count":8,"area":null},{"id":"2311.18029","title":"Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields","created":"2023-11-29","cited_by_count":8,"area":null},{"id":"2312.03998","title":"Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification","created":"2023-12-07","cited_by_count":8,"area":null}],"next_cursor":"100"}