Good Papers: read the good ones

Showing Time series forecasting Show all papers

76%Highly rated

?Highly ratedVote to see the score

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

– ReadersNo votes yet

10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it

lenient 5/5

medium 5/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Tel Aviv Princeton Time 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026SpotlightU Science and Technology of Chin Northeastern Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026U Pittsburgh Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Reykjavik Reykjavík Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Tsinghua Tsinghua University, Tsinghua New York Infinigence AI Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Xi'an Jiaotong Harbin Institute of Technology Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Shandong University; infec Shandong Tianjin Hong Kong Polytechnic China University of Petroleum Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026UNIST | Ulsan National Institute Korea Advanced Institute of Scie Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Yantai Soochow Ocean University of China Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026U Melbourne The University of Melbourne Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026SpotlightBeijing Institute of Technology U Central Florida U Technology Sydney Time 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026KAIST Georgia Tech Time 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Southern University of Science a Shenzhen University of Advanced The Chinese University of Hong K Omni Intelligence; SUSTech Time 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026SpotlightEast China Normal Xian Polytechnic Aalborg Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026U New South Wales U Science and Technology of Chin UNSW Sydney Hong 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Zhejiang Fuzhou Hangzhou Dianzi Beijing University of Post and T College 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Chung-ang LG AI Research Korea Time 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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

67%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026East China Normal CFETS CFETSIT Cfetsit Aalborg Time 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

– ReadersNo votes yet

2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it

lenient 2/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026U Electronic Science and Technol UESTC Time 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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Technical University of Munich AWS AI Keystone AI Amazon Prior Labs Time series forecasting

fev-bench: A Realistic Benchmark for Time Series Forecasting

Oleksandr Shchur, Abdul Fatir Ansari, Ali Caner Turkmen, Lorenzo Stella and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 179

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Flatiron Institute Simplex New York Time series forecasting

Linear approximations to HMM filtering

Andrew Mah, Joshua L Pughe-Sanford, Sarah Harvey, Alex Williams

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Harbin Engineering Queen Mary, University of London Time series forecasting

Diff-Kalman: Difference-Driven Learning for Structure-Preserving Kalman Filtering

Jia Gao, Xianglei Xing, yang qing, Tianshuo Zhang and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026U Science and Technology of Chin Electric Power Research Institut Time series forecasting

Time–Frequency Non-Stationary Modeling for Multivariate Time Series Forecasting

Haoyi Zhao, Yishan Jiang, Jiqian Yang, Ji Chang and 2 more

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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026CNRS LAGEPP Lyon1 Mines Paris - PSL L2S, CNRS Mines ParisTech Naver Labs Europe Time series forecasting

Provable State Estimation with Recurrent Models

Vincent Andrieu, Pauline Bernard, Lucas Brivadis, Laurent Praly and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Ben Gurion University of the neg Ben Gurion Amazon Ben-Gurion University of the Neg Time series forecasting

Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting

Raz Marshanski, Liran Nochumsohn, Mayank Jauhari Iitr, Boris Oreshkin and 1 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Nanjing Time series forecasting

Parallel-in-Time Variational Inference for Latent Stochastic Differential Equations

Chenyang Wu, Pengfei Liu, Zongzhang Zhang

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Fudan Time series forecasting

SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting

Bang Hu, Changze Lv, Junyi Wang, mingjieli and 3 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Ludong Shandong Technology and Business Time series forecasting

TailFix: Mitigating Error Accumulation and Correcting Tail Deterioration in Long-Horizon Forecasting

Hua Wang, Haijing Gao, Fan Zhang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Ludong Shandong Technology and Business Time series forecasting

DSR-TSF: Spectrum-Driven Dynamic Routing for Efficient Long-Horizon Time Series Forecasting

Hua Wang, Bing Li, Fan Zhang

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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it

lenient 1/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026ludong Shandong Technology and Business Time series forecasting

FISC: Time-Series Forecasting via First-Layer Statistical Calibration Constraints

Hua Wang, Xiyuan Zhang, Fan Zhang

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026ludong Shandong Technology and Business Time series forecasting

BucpTSF: Breaking the Uniform Computation Paradigm in Time-Series Forecasting

Hua Wang, Jinghao Lu, Fan Zhang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026McGill McGill ILLS and MILA | CNRS Paris-Sacl Amazon Time series forecasting

DynaPFN: Zero-Shot Dynamical System Forecasting with Tabular Prior-Fitted Networks

Chiara Roverato, Joseph Cotnareanu, Pablo Piantanida, Boris Oreshkin and 1 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Karlsruhe Institute of Technolog McGill KIT Amazon Time series forecasting

SLiDE: Structured Linear Dynamics for Forecasting with Exogenous Inputs

Sebastian Pütz, Theodore Glavas, Benjamin Schäfer, Boris Oreshkin and 1 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026SpotlightShandong Technology and Business Time series forecasting

Volatility-Whitened Probabilistic Residual Modeling for Long-Term Time Series Forecasting

Fan Zhang, Shiming Fan, Zexuan Ma, Shijun Chen and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Shandong Technology and Business Time series forecasting

QWaveNet: Quantum-Enhanced Wavelet Network for Time Series Forecasting

Fan Zhang, Shijun Chen, Meijia Wang, Shiming Fan and 2 more

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026U Southern California Time series forecasting

Preferential dynamic modeling with forward-backward smoothing

Omid G. Sani, Trisha Jha, Mohammad Hosseini, Maryam Shanechi

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Aalto U Helsinki Time series forecasting

Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting

Ashutosh Vaishnav, Mohsen Amidzadeh, Teemu Kämäräinen, Matti Siekkinen and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Yeshiva Time series forecasting

Learning Digital Twins under Drift: Optimal Tracking Rates for Non-Stationary Dynamical Systems

David Li, Honggang Wang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Applied Artificial Intelligence Deakin Time series forecasting

SteerCast: Retrieval-Based Latent Steering for Decoder-Only Time Series Forecasting

Van Dai Do, Huu H Nguyen, Minh Hoang Nguyen, Hung Le

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026DeepAuto.ai KAIST KAIST, AITRICS Time series forecasting

Visual Anchoring for Scenario-Guided Forecasting

Patara Trirat, Jay Heo, Heejun Lee, Sung Ju Hwang

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Central South National University of Defense T Time series forecasting

WILD: Widely Linear Conditioning for Time Series Forecasting

Binli Luo, Wanrong Ma, Ning Gui, Xianhan Tan

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Central South Time series forecasting

Knowing When Multivariate Forecasts Are Wrong

Binli Luo, Ning Gui, Xianhan Tan

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

57%Worth a look

?Worth a lookVote to see the score

NeurIPS 2026Zhejiang University of Technolog Time series forecasting

$PAS^2$: Physics-Anchored Spectral Reasoning for Air Quality Forecasting

Haofeng Ying, Wenbin Lu, Wenxin Shen, Junnan Xu and 1 more

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

– ReadersNo votes yet

1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

45%Niche pick

?Niche pickVote to see the score

NeurIPS 2026Fudan Time series forecasting

SDHilb: Schrödinger Dynamics-Guided Neural Network with Adaptive Multi-Scale Hilbert Transform for Time Series Forecasting

Caihua He, Weiyang Ding

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

– ReadersNo votes yet

0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it

lenient 0/5

medium 0/10

strict 0/5

74%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026U Washington Massachusetts Institute of Techn Time series forecasting

Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach

Adaptive robust optimization builds time-varying linear ensembles of forecasting models that reduce error by 16, 26% and risk by 14, 28% versus best single members and competing methods.

Leonard Boussioux, Henry Mao, Dimitris Bertsimas

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

– ReadersNo votes yet

9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it

lenient 5/5

medium 3/10

strict 1/5

83%Must read

?Must readVote to see the score

NeurIPS 2026Tsinghua Huazhong University of Science a Ant International Ant Group Time series forecasting

Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting

Timeflies jointly infers whether future observations exist and predicts their values, outperforming methods that assume future observation times are known.

Yifan Hu, Hongzhou Chen, Peiyuan Liu, Yiding Liu and 2 more

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

– ReadersNo votes yet

13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it

lenient 5/5

medium 8/10

strict 0/5

78%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026University Koblenz Koblenz Universität Koblenz Time series forecasting

Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

Observable Neural ODEs link control-theoretic observability to causal identifiability in continuous-time settings with hidden confounders and outperform recent sequence models in forecasting under alternative treatments.

Jennifer Wendland, Nicolas Freitag, Maik Kschischo

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet

11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it

lenient 3/5

medium 7/10

strict 1/5

83%Must read

?Must readVote to see the score

NeurIPS 2026Interdisciplinary Center for Sci U Tübingen IWR, Heidelberg Heidelberg University, Ruprecht-Time series forecasting

The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling

Finite-horizon probabilistic training creates a consistency gap that decouples uncertainty from local dynamics in chaotic surrogates; a Kalman-aware framework evaluating local innovations while transporting covariance through learned Jacobians closes it.

Andre Herz, Matthijs Pals, Daniel Durstewitz, Georgia Koppe

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

– ReadersNo votes yet

13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it

lenient 2/5

medium 10/10

strict 1/5

74%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026SpotlightCentral Institute of Mental Heal Ruprecht-Karls-Universität Heide IWR, Heidelberg Time series forecasting

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction

DEER with generalized teacher forcing trains recurrent networks in parallel time to learn nonlinear dynamics on long sequences, outperforming linear state-space models for systems with long time scales.

Florian Hess, Florian Götz, Daniel Durstewitz

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

– ReadersNo votes yet

9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it

lenient 3/5

medium 6/10

strict 0/5

72%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026U Southern California Carnegie Mellon USC Time series forecasting

Learning Fractional-Order Dynamics from a Single Trajectory

FO-GS estimates discrete-time fractional-order linear dynamics from one trajectory with both errors scaling as O(t^{-1/2}).

Xiaole Zhang, Ziyi Zhang, zehao zhao, Stephen Tu and 3 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet

8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it

lenient 3/5

medium 3/10

strict 2/5

71%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026LUT Lappeenranta-Lahti University of Politecnico di Milano Time series forecasting

A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures

A Schur-decomposition-based projection ensures asymptotic stability in state-space neural networks with minimal overparameterization, matching state-of-the-art accuracy and convergence.

Sergio Mauricio Vanegas Arias, Lasse Lensu, Fredy Ruiz

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

6/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

80%Must read

?Must readVote to see the score

NeurIPS 2026Ruprecht-Karls-Universität Heide Central Institute of Mental Heal Central Institute Of Mental Heal IWR, Heidelberg Time series forecasting

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction

This paper fixes structural mismatches in dynamical system reconstruction via feature splitting to enable zero-shot out-of-domain forecasting across tipping points with derived extrapolation bounds.

Georg Trede, Charlotte Doll, Elias Daniel Weber, Daniel Durstewitz

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

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

88%Must read

?Must readVote to see the score

NeurIPS 2026Volkswagen AG Technical University of Munich Arnold Jäger Holding Volkswagen Group Group Innovation Time series forecasting

A Scalable Multi-Task Model for Virtual Sensors

A multi-task virtual sensor model predicts diverse targets via shared representations, reducing computation up to 415x and memory 951x while improving accuracy over isolated and foundation alternatives.

Leon Götz, Lars Frederik Peiss, Erik Sauer, Andreas U Sass and 3 more

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

– ReadersNo votes yet

15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it

lenient 5/5

medium 7/10

strict 3/5

83%Must read

?Must readVote to see the score

NeurIPS 2026The University of Tokyo U Electronic Science and Technol Tokyo Time series forecasting

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning

TimeClaw learns reusable hierarchical experience from exploratory time-series executions via explore-compare-distill-reinject loops, improving reasoning without online adaptation.

Hangchen Liu, Dongyuan Li, Renhe Jiang, Jiewen Deng and 2 more

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

– ReadersNo votes yet

13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it

lenient 5/5

medium 8/10

strict 0/5

78%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Mila - Quebec Artificial Intelli U Pennsylvania, University of Pe NVIDIA Mila / École Polytechnique de Mo Google DeepMind Time series forecasting

Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems

Sparse Koopman autoencoders use sparse latent supports as label-free regime indicators that identify local dynamical basins and outperform dense autoencoders in multibasin forecasting.

Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi, Sarath Chandar and 1 more

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

– ReadersNo votes yet

11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it

lenient 3/5

medium 8/10

strict 0/5

74%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Imperial College London Imperial College London, Nanyang Time series forecasting

End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems

A flow-based estimator enables exact likelihood optimization for recurrent switching dynamical systems, proving broad identifiability and improving disentanglement and forecasting over VAE methods.

Carles Balsells Rodas, Zhengrui Xiang, Francisco Sumba Toral, Yingzhen Li

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

– ReadersNo votes yet

9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

71%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Universität Hildesheim U Hildesheim Time series forecasting

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting

CircuITS uses probabilistic circuits for irregular multivariate time series forecasting to guarantee valid joint distributions and improve density estimation accuracy.

Christian Klötergens, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi

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

– ReadersNo votes yet

7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it

lenient 3/5

medium 3/10

strict 1/5

78%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026The Francis Crick Institute ETHZ - ETH Zurich Time series forecasting

Curriculum Multiple Shooting for Robust Training of Neural and Universal Differential Equations

Curriculum multiple shooting integrates curriculum learning with multiple shooting to robustly train neural and universal differential equations, accelerating convergence and improving generalization across benchmarks.

Sebastian Persson, Giacomo Fabrini, Branwen Snelling, Fabian Fröhlich

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

– ReadersNo votes yet

11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it

lenient 5/5

medium 5/10

strict 1/5

88%Must read

?Must readVote to see the score

NeurIPS 2026U Kentucky , University of Kentucky Time series forecasting

RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

RESCAST-100K introduces a 100,000-home benchmark for cross-domain residential load and temperature forecasting, with cross-attention and MLP-mixer models outperforming recurrent baselines under domain shift.

Jainam Dhruva, Yousaf Raza, A.B. Siddique, Simone Silvestri

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet

15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it

lenient 5/5

medium 8/10

strict 2/5

Show 20 more papers