| A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction | Sep 26, 2024 | Mixture-of-ExpertsPrediction | CodeCode Available | 1 | 5 |
| Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains | Aug 12, 2018 | AllClassification | CodeCode Available | 1 | 5 |
| Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values | Feb 21, 2020 | Time SeriesTime Series Analysis | CodeCode Available | 1 | 5 |
| Evaluation of post-hoc interpretability methods in time-series classification | Feb 11, 2022 | ClassificationTime Series | CodeCode Available | 1 | 5 |
| CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery | Oct 3, 2023 | BenchmarkingCausal Discovery | CodeCode Available | 1 | 5 |
| Intrinsic persistent homology via density-based metric learning | Dec 11, 2020 | Anomaly DetectionMetric Learning | CodeCode Available | 1 | 5 |
| CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting | Sep 27, 2024 | Anomaly DetectionTime Series | CodeCode Available | 1 | 5 |
| A Deep Learning Approach to Analyzing Continuous-Time Systems | Sep 25, 2022 | Deep LearningTime Series | CodeCode Available | 1 | 5 |
| Highly comparative time-series analysis: The empirical structure of time series and their methods | Apr 3, 2013 | Time SeriesTime Series Analysis | CodeCode Available | 1 | 5 |
| Human Activity Segmentation Challenge @ ECML/PKDD’23 | Sep 18, 2023 | Activity RecognitionChange Point Detection | CodeCode Available | 1 | 5 |
