{"total":18,"rows":[{"id":"2109.12218","title":"Long-Range Transformers for Dynamic Spatiotemporal Forecasting","created":"2021-09-24","cited_by_count":46,"area":null},{"id":"2305.14582","title":"Interpretation of Time-Series Deep Models: A Survey","created":"2023-05-23","cited_by_count":15,"area":null},{"id":"2201.04895","title":"Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning","created":"2022-01-13","cited_by_count":5,"area":null},{"id":"2205.13012","title":"TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series","created":"2022-05-25","cited_by_count":4,"area":null},{"id":"2210.04417","title":"Self-explaining Hierarchical Model for Intraoperative Time Series","created":"2022-10-10","cited_by_count":4,"area":null},{"id":"2510.16511","title":"Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection","created":"2025-10-18","cited_by_count":4,"area":null},{"id":"2201.01869","title":"Earthquake Nowcasting with Deep Learning","created":"2021-12-18","cited_by_count":3,"area":null},{"id":"2407.09538","title":"A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction","created":"2024-06-26","cited_by_count":2,"area":null},{"id":"2110.01659","title":"Cross-Modal Virtual Sensing for Combustion Instability Monitoring","created":"2021-10-04","cited_by_count":1,"area":null},{"id":"2505.19159","title":"A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation","created":"2025-05-25","cited_by_count":1,"area":null},{"id":"2507.09445","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","created":"2025-07-13","cited_by_count":1,"area":null},{"id":"2108.09659","title":"Evolutionary Ensemble Learning for Multivariate Time Series Prediction","created":"2021-08-22","cited_by_count":0,"area":null},{"id":"2206.03304","title":"On the balance between the training time and interpretability of neural ODE for time series modelling","created":"2022-06-07","cited_by_count":0,"area":null},{"id":"2407.17654","title":"Generative Learning for Simulation of Vehicle Faults","created":"2024-07-24","cited_by_count":0,"area":null},{"id":"2501.04339","title":"Interpretable deep convolutional model for nonlinear multivariate time series in complex systems","created":"2025-01-08","cited_by_count":0,"area":null},{"id":"2504.18329","title":"PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2601.20448","title":"TimeCatcher: A Variational Framework for Volatility-Aware Forecasting of Non-Stationary Time Series","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2603.03902","title":"PatchDecomp: Interpretable Patch-Based Time Series Forecasting","created":"2026-03-04","cited_by_count":0,"area":null}],"next_cursor":null}