The paper introduces a hybrid attention model that learns a unified time‑aware patch representation for irregular multivariate time series (IMTS) forecasting. It employs a time‑aware patch encoding to embed variable‑length intra‑patch timestamps, a time bias attention mechanism to adjust for temporal misalignment and asynchronous cross‑channel dependencies, and a hybrid causal mask on a decoder‑only Transformer to balance historical context with autoregressive forecasting. The authors also curate VersaTSA, a 30 B‑observation dataset preserving native sampling sparsity, and demonstrate state‑of‑the‑art zero‑shot performance on three IMTS benchmarks while remaining competitive on regular MTS tasks.
By Zhihao Lin, Li Lin, Qi Zhang, Kaiwen Xia, Shuai Wang, Jialin Qiao
SACQ introduces a structured prediction head for long‑horizon time‑series forecasting that replaces the traditional flatten readout. It uses a two‑stage decoding process: a coarse patch‑grid scaffold is first generated, then each future position is refined via cross‑attention over historical memory, with a learned gate merging the corrections. A batch‑adaptive scaled log‑cosh loss further stabilizes training under long horizons and noisy labels, leading to superior MSE/MAE performance across multiple backbones with minimal overhead.
By Guo Cheng, Zhengzhuo Xu, Chenchen Jing, Jingyi Hou
arXiv:2610.03494v1 Announce Type: new
Abstract: Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidenc...
By Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
arXiv:2405. 16440v2 Announce Type: replace-cross Abstract: In recent years, Transformers have become the de-facto architecture for long-term time series forecasting (LTSF), yet they face challenges associated with the self-attention mechanism, including quadratic complexity and permutation-invariant bias.
By Xiuding Cai, Xueyao Wang, Yaoyao Zhu, Yu Yao
arXiv:2609.06006v1 Announce Type: cross
Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...
By Minh Hoang Nguyen, Huu Hiep Nguyen, Manh Nguyen, Van Dai Do, Dung Nguyen, Hung Le
Dual-Context Analog Retrieval for Time Series Forecasting (DuoTS) introduces a two-stage forecasting approach that first generates a base forecast and then refines it patch by patch. Each refinement step fuses a current context, which focuses on recent tokens, with a detail context that incorporates retrieved analogs and their subsequent trajectories. Experiments on real-world datasets demonstrate that DuoTS achieves state‑of‑the‑art performance, and ablation studies confirm the importance of both contexts and the refinement mechanism.
By Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
arXiv:2607. 00197v1 Announce Type: new Abstract: Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation.
By Haroon Gharwi, Yue Dai, Kai Shu
arXiv:2607. 14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable.
By Xiangni Tian, Kaixian Yu, Runpeng Dai, Niansheng Tang, Hongtu Zhu
Fast Weight Attention for Continual Learning introduces recurrent fast‑weight memories and selective state‑space models that compress expanding context into a fixed‑size recurrent state, enabling an online learning rule for state transitions. The paper derives normalized first‑order updates for squared‑error regression and negative inner‑product objectives, presenting several variants (Falcon‑1, Falcon‑2, Falcon‑3 and their inner‑product counterparts) with recurrent, masked‑parallel, and chunk‑parallel implementations. These methods demonstrate competitive performance in language modeling and improved length extrapolation on variable‑digit addition tasks.
By Yifan Zhang, Steve Ta, Jasper Zhang, Jichen Feng, Shuzhen Li, Yongxin Zhang, Yifeng Liu, Huizhuo Yuan, Mengdi Wang, Quanquan Gu, Andrew Chi-Chih Yao
arXiv:2610.10323v1 Announce Type: new
Abstract: Short-horizon realized volatility forecasting requires the integration of market information that evolves at incompatible temporal resolutions, from se...
By Mihai Bogdan Deaconu, Ioan Daniel Pop
arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?
By Hongbo Liu, Jia Xu
The paper introduces the Progressive Memory Transformer (PMT), a transformer variant that adds writable, window‑aligned memory to expose mid‑range representations alongside token and sequence‑level outputs. PMT is trained with a hierarchical learning framework that applies separate objectives at local, mid‑range, and global scales, encouraging the model to capture fine‑grained variation, window‑level motifs, and overall sequence agreement. Experiments on seven UCR/UEA/UCI classification datasets, a cue‑retention probe, and forecasting tasks show that PMT achieves strong low‑label classification performance, competitive multi‑horizon forecasting, and evidence that its memory states encode mid‑range motifs.
By Tord Sture Stangeland, Andreas K\"ohler, Steffen M{\ae}land, Ad\'in Ram\'ires Rivera