arXiv:2609.08286v1 Announce Type: new
Abstract: Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained l...
By Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
arXiv:2610.15760v1 Announce Type: cross
Abstract: Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-...
By Sarang Rajendra Patil, Vishal Fenn, Mengjia Xu
arXiv:2608. 08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality.
By Xuan-May Le, Minh-Tuan Tran, Ling Luo, Uwe Aickelin, Dinh Phung, Trung Le
The paper introduces a graph deep learning framework that learns to forecast spatiotemporal data across multiple hierarchical structures, using variations among hierarchies as multi‑task regularization. By jointly training on different aggregation levels, the model reduces over‑specialization and improves forecasting accuracy. A novel top‑down operator ensures coherent forecasts with linear time and space complexity, making the approach computationally efficient.
By Valentina Moretti, Cesare Alippi, Andrea Cini
arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.
By Ruchi Pakhle
LagNet introduces LagLayer, a Toeplitz‑structured operator that ties history‑to‑future connections by relative lag, replacing traditional temporal linear layers in forecasting models. By stacking LagLayers with activations and multiple feature maps, LagNet achieves competitive performance on long‑term time series benchmarks. Sensitivity matrix diagnostics and limited‑data experiments show that LagLayer offers a sample‑efficient alternative to linear layers.
By Zhenan Yu, Guangxin Jiang, Jiuyun Jiang, Jin Yang