{"total":21,"rows":[{"id":"2406.14231","title":"aeon: a Python toolkit for learning from time series","created":"2024-06-20","cited_by_count":47,"area":null},{"id":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2402.14041","title":"E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series","created":"2024-02-21","cited_by_count":7,"area":null},{"id":"2502.05392","title":"Open Challenges in Time Series Anomaly Detection: An Industry Perspective","created":"2025-02-08","cited_by_count":5,"area":null},{"id":"2310.20431","title":"Raising the ClaSS of Streaming Time Series Segmentation","created":"2023-10-31","cited_by_count":3,"area":null},{"id":"2504.01783","title":"CLaP -- State Detection from Time Series","created":"2025-04-02","cited_by_count":3,"area":null},{"id":"2408.00041","title":"Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification","created":"2024-07-31","cited_by_count":2,"area":null},{"id":"2408.00856","title":"Penalty Learning for Optimal Partitioning using Multilayer Perceptron","created":"2024-08-01","cited_by_count":2,"area":null},{"id":"2404.16886","title":"Review of Data-centric Time Series Analysis from Sample, Feature, and Period","created":"2024-04-24","cited_by_count":1,"area":null},{"id":"2509.03649","title":"An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification","created":"2025-09-03","cited_by_count":1,"area":null},{"id":"2510.23261","title":"Toward Interpretable Evaluation Measures for Time Series Segmentation","created":"2025-10-27","cited_by_count":1,"area":null},{"id":"2303.11647","title":"Are uGLAD? Time will tell!","created":"2023-03-21","cited_by_count":0,"area":null},{"id":"2311.02561","title":"Ego-Network Transformer for Subsequence Classification in Time Series Data","created":"2023-11-05","cited_by_count":0,"area":null},{"id":"2405.16164","title":"Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2412.10814","title":"Learning Satellite Pattern-of-Life Identification: A Diffusion-based Approach","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2412.11800","title":"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data","created":"2024-12-16","cited_by_count":0,"area":null},{"id":"2505.07413","title":"Learning Penalty for Optimal Partitioning via Automatic Feature Extraction","created":"2025-05-12","cited_by_count":0,"area":null},{"id":"2505.17640","title":"A Network Science Approach to Granular Time Series Segmentation","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2511.14945","title":"Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities","created":"2025-11-18","cited_by_count":0,"area":null},{"id":"2609.24278","title":"High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.30790","title":"Towards Universal Representation-Based Process Control","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}