{"total":43,"rows":[{"id":"2506.18046","title":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods","created":"2025-06-22","cited_by_count":60,"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":"2510.23672","title":"DBLoss: Decomposition-based Loss Function for Time Series Forecasting","created":"2025-10-27","cited_by_count":45,"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":"2503.00877","title":"Patch-wise Structural Loss for Time Series Forecasting","created":"2025-03-02","cited_by_count":35,"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":"2603.08032","title":"GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables","created":"2026-03-09","cited_by_count":13,"area":null},{"id":"2506.23121","title":"CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation","created":"2025-06-29","cited_by_count":12,"area":null},{"id":"2505.23017","title":"$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting","created":"2025-05-29","cited_by_count":8,"area":null},{"id":"2509.23313","title":"ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting","created":"2025-09-27","cited_by_count":8,"area":null},{"id":"2504.09993","title":"AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification","created":"2025-04-14","cited_by_count":6,"area":null},{"id":"2510.12681","title":"CoRA: Covariate-Aware Adaptation of Time Series Foundation Models","created":"2025-10-14","cited_by_count":6,"area":null},{"id":"2602.18473","title":"Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series","created":"2026-02-09","cited_by_count":5,"area":null},{"id":"2505.11781","title":"Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting","created":"2025-05-17","cited_by_count":4,"area":null},{"id":"2512.14253","title":"FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2603.21612","title":"Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction","created":"2026-03-23","cited_by_count":3,"area":null},{"id":"2606.02670","title":"Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate","created":"2026-06-01","cited_by_count":3,"area":null},{"id":"2511.22853","title":"TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE","created":"2025-11-28","cited_by_count":2,"area":null},{"id":"2602.13783","title":"MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models","created":"2026-02-14","cited_by_count":2,"area":null},{"id":"2506.01290","title":"Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment","created":"2025-06-02","cited_by_count":1,"area":null},{"id":"2506.02576","title":"ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2512.14718","title":"SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2605.08289","title":"What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.17730","title":"L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2608.05742","title":"Multivariate Time Series Forecasting needs Cross Variable Loss","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2504.14178","title":"Segregation and Context Aggregation Network for Real-time Cloud Segmentation","created":"2025-04-19","cited_by_count":0,"area":null},{"id":"2505.12761","title":"Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2507.00701","title":"SCAWaveNet: A Spatial-Channel Attention-Based Network for Global Significant Wave Height Retrieval","created":"2025-07-01","cited_by_count":0,"area":null},{"id":"2508.04503","title":"PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2509.12196","title":"Dynamic Relational Priming Improves Transformer in Multivariate Time Series","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2509.17063","title":"TSGym: Design Choices for Deep Multivariate Time-Series Forecasting","created":"2025-09-21","cited_by_count":0,"area":null},{"id":"2510.20302","title":"InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modeling","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2511.20577","title":"MSTN: A Lightweight and Fast Model for General TimeSeries Analysis","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.16632","title":"Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2603.01101","title":"SyncTrack: Rhythmic Stability and Synchronization in Multi-Track Music Generation","created":"2026-03-01","cited_by_count":0,"area":null},{"id":"2603.08753","title":"Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2603.19899","title":"Deep Time-Series Forecasting in 10 Years: A Survey","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2606.04752","title":"An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2607.08234","title":"RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.19670","title":"CoRe: Coherence and Relational Alignment for Multivariate Time Series Forecasting","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.24441","title":"MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.34638","title":"Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.34939","title":"XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}