{"total":19,"rows":[{"id":"2308.03274","title":"DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction","created":"2023-08-07","cited_by_count":49,"area":null},{"id":"2412.05244","title":"Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization","created":"2024-12-06","cited_by_count":27,"area":null},{"id":"2305.10721","title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","created":"2023-05-18","cited_by_count":11,"area":null},{"id":"2308.01011","title":"Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach","created":"2023-08-02","cited_by_count":5,"area":null},{"id":"2407.19784","title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2410.17159","title":"LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2410.22649","title":"WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting","created":"2024-10-30","cited_by_count":3,"area":null},{"id":"2409.11024","title":"D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding","created":"2024-09-17","cited_by_count":2,"area":null},{"id":"2602.06323","title":"How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting","created":"2026-02-06","cited_by_count":2,"area":null},{"id":"2602.08262","title":"Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2305.18382","title":"Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers","created":"2023-05-28","cited_by_count":1,"area":null},{"id":"2312.03796","title":"Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series","created":"2023-12-06","cited_by_count":1,"area":null},{"id":"2406.03710","title":"TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting","created":"2024-06-06","cited_by_count":1,"area":null},{"id":"2602.18465","title":"Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2307.09019","title":"U-shaped Transformer: Retain High Frequency Context in Time Series Analysis","created":"2023-07-18","cited_by_count":0,"area":null},{"id":"2503.00036","title":"A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2511.00747","title":"Effective Series Decomposition and Components Learning for Time Series Generation","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2511.13510","title":"Naga: Vedic Encoding for Deep State Space Models","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2609.34652","title":"Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}