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arXivTime series forecasting

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

SEER uses self-evolving event reasoning and retrieval to dynamically optimize forecasting with exogenous events via reflective memory and causal knowledge, outperforming state-of-the-art baselines.

Mingtian Tan, Palash Goyal, Mihir Parmar, Sarkar Snigdha Sarathi Das and 5 more

Published Oct 2, 2026 · ▲ 13 on Hugging Face · Code

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NeurIPS 2026Tel AvivPrincetonTime series forecasting

A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems

Yuval Ran-Milo, Angelos Assos, Elad Hazan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightU Science and Technology of ChinNortheasternTime series forecasting

Beyond Linear Decoders: Dynamic Expert-Coupled Optimal Decoding for Time Series Forecasting

Binwu Wang, Zhipeng Liu, Zhengyang Zhou, Pengkun Wang and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U PittsburghTime series forecasting

CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction

Ankitkumar Joshi, Milos Hauskrecht

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026ReykjavikReykjavíkTime series forecasting

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters

Hugo Cazaux, Eyjolfur Asgeirsson, Hlynur Stefansson

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TsinghuaTsinghua University, TsinghuaNew YorkInfinigence AITime series forecasting

Decomposing Temporal and Job-Induced Dynamics for Probabilistic Computing Workload Forecasting via Graph-Conditioned Dual-Branch Diffusion

Baozhen Luo, Minbo Ma, Honglin Zhang, Yuan Yuan and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Xi'an JiaotongHarbin Institute of TechnologyTime series forecasting

ToLD: Efficient Time Series Forecasting via Tokenized Truncated Latent Diffusion

Jiayi Tian, Jiaze Wang, Wenzhe zhao, Tian Xia and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shandong University; infecShandongTianjinHong Kong PolytechnicChina University of PetroleumTime series forecasting

IMTS-Tokenizer: Time-Aware Tokenization for Irregular Multivariate Time Series Forecasting

Bin Xu, Yinghua Li, Linqi Han, Xiaoyu Li and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026UNIST | Ulsan National InstituteKorea Advanced Institute of ScieTime series forecasting

OliO: ODE-based Linear Transition Operator for Self-Supervised Time Series Forecasting

Kwangryeol Park, Seulki Lee

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026YantaiSoochowOcean University of ChinaTime series forecasting

OTformer: Non-Stationarity-Aware Adaptive Optimal Transport Attention for Time Series Forecasting

Zhaowei Liu, Kaixiang Wang, Sheng Liu, zengyang zhang and 7 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U MelbourneThe University of MelbourneTime series forecasting

Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting

Songlin Du, Ling Luo, Uwe Aickelin

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightBeijing Institute of TechnologyU Central FloridaU Technology SydneyTime series forecasting

Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting

Jingru Fei, Kun Yi, Wei Fan, Qi Zhang and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KAISTGeorgia TechTime series forecasting

TimeOperator: A Function-to-Function Approach to Time Series Modeling

SheoYon Jhin, B. Aditya Prakash, Noseong Park

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Southern University of Science aShenzhen University of Advanced The Chinese University of Hong KOmni Intelligence; SUSTechTime series forecasting

Predictive Representation Learning for Partially Observed Neural Dynamics

Xinyi Li, Zhichao Liang, Hongjun Jiang, Yian Zhu and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightEast China NormalXian PolytechnicAalborgTime series forecasting

Rubato: Signature Attention for Irregular Multivariate Time Series Forecasting

Jintao Yang, Meng Wang, Chenjuan Guo, Bin Yang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U New South WalesU Science and Technology of ChinUNSW SydneyHong Kong University of Science Time series forecasting

Robust Residual Correction via Selective Deployment for Time Series Forecasting

Jianxiang Xie, YUNCHENG HUA, Mingyue Cheng, Flora Salim and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ZhejiangFuzhouHangzhou DianziBeijing University of Post and TCollege of Computer Science and Time series forecasting

OrangeTree: A Linear and Tree-based Time Series Forecasting Model Supporting Multiple Input and Output Lengths

Yiqi Tang, Zhichen Lai, Yuxuan Yang, Dalin Zhang and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Chung-angLG AI ResearchKoreaTime series forecasting

Safe Active Learning with Future Viability Guarantees in Time-Series Models

Hyeonjun Park, Whiyoung Jung, Deunsol Yoon, Sunghoon Hong and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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67%Highly rated
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NeurIPS 2026East China NormalCFETSCFETSITCfetsitAalborgTime series forecasting

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting

Xiangfei Qiu, Liu Yang, Xiangyu Xu, Hanyin Cheng and 8 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Electronic Science and TechnolUESTCTime series forecasting

MDK-MoE: Multi-view Decomposed Kalman Mixture of Experts Framework for Non-stationary Time Series Forecasting

Rui Hou, Yao Liu, Ruilin Jiang, Mengyao Lu and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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