ICLR2025已经结束了讨论阶段，进入了meta-review阶段，分数应该不会有太大的变化了，本文总结了其中时间序列(Time Series)高分的论文。如有疏漏，欢迎大家补充。

挑选原则：均分要大于等于6（≥6，即使有3，但是有8或者更高的分拉回来也算）

时间序列Topic：预测，插补，分类，生成，因果分析，异常检测，LLM以及基础模型（还有KAN和Mamba各一篇）等内容。总计32篇

1. TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
2. Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery
3. Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
4. Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
5. Label Correlation Biases Direct Time Series Forecast
6. Fast and Slow Streams for Online Time Series Forecasting Without Information Leakage
7. Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning
8. Optimal Transport for Time Series Imputation
9. Constrained Posterior Sampling: Time Series Generation with Hard Constraints
10. A Simple Baseline for Multivariate Time Series Forecasting
11. Shedding Light on Time Series Classification using Interpretability Gated Networks
12. Multi-Resolution Decomposable Diffusion Model for Non-Stationary Time Series Anomaly Detection
13. CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching
14. CoMRes: Semi-Supervised Time Series Forecasting Utilizing Consensus Promotion of Multi-Resolution
15. Towards Neural Scaling Laws for Time Series Foundation Models
16. Quantifying Past Error Matters: Conformal Inference for Time Series
17. TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation
18. In-context Time Series Predictor
19. Compositional simulation-based inference for time series
20. Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
21. TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting
22. Investigating Pattern Neurons in Urban Time Series Forecasting
23. Locally Connected Echo State Networks for Time Series Forecasting
24. Diffusion-based Decoupled Deterministic and Uncertain Framework for Probabilistic Multivariate Time Series Forecasting
25. TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
26. Exploring Representations and Interventions in Time Series Foundation Models
27. FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
28. KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Processes for Time Series Forecasting
29. Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series
30. TwinsFormer: Revisiting Inherent Dependencies via Two Interactive Components for Time Series Forecasting
31. DyCAST: Learning Dynamic Causal Structure from Time Series
32. Drift2Matrix: Kernel-Induced Self Representation for Concept Drift Adaptation in Co-evolving Time Series

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1 TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

链接：https://openreview.net/forum?id=1CLzLXSFNn

分数：6810

关键词：多任务（预测，分类，插补，异常检测），基础模型

keywords：time series, pattern machine, predictive analysis

TL; DR：TimeMixer++ is a time series pattern machine that employs multi-scale and multi-resolution pattern extraction to deliver SOTA performance across 8 diverse analytical tasks, including forecasting, classification, anomaly detection, and imputation.

2 Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery

链接：https://openreview.net/forum?id=k38Th3x4d9

分数：88888

关键词：因果发现

keywords：root cause analysis, Granger causality, multivariate time series

3 Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series

链接：https://openreview.net/forum?id=8zJRon6k5v

分数：8888

关键词：变分推断，不规则时间序列，状态空间模型

keywords：stochastic optimal control, variational inference, state space model, irregular time series

TL; DR：We propose a multi-marginal Doob’s $h$-transform for irregular time series and variational inference with stochastic optimal control to approximate it.

4 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

链接：https://openreview.net/forum?id=e1wDDFmlVu

分数：688

关键词：预测，基础模型，混合专家系统

keywords：time series, foundation model, forecasting

5 Label Correlation Biases Direct Time Series Forecast

链接：https://openreview.net/forum?id=4A9IdSa1ul

分数：8686

关键词：长时预测，频域

keywords：Time series, Long-term Forecast

TL; DR：Learning to forecast in the frequency domain significantly enhances forecasting performance.

6 Fast and Slow Streams for Online Time Series Forecasting Without Information Leakage

链接：https://openreview.net/forum?id=I0n3EyogMi

分数：6688

关键词：在线预测，流式数据，概念飘逸

keywords：online time series forecasting, concept drift, online learning

TL; DR： Redefined the setting of online time series forecasting to prevent information leakage and proposed a model-agnostic framework for this setting.

7 Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning

链接：https://openreview.net/forum?id=nibeaHUEJx

分数：6688

关键词：频域，平移不变性

keywords：Time series analysis, invariance in neural networks

8 Optimal Transport for Time Series Imputation

链接：https://openreview.net/forum?id=xPTzjpIQNp

分数：588

关键词：插补，最优传输

keywords：Time series, Imputation

9 Constrained Posterior Sampling: Time Series Generation with Hard Constraints

链接：https://openreview.net/forum?id=pKMpmbuKnd

分数：5688

关键词：时间序列生成，扩散模型

keywords：Time Series Generation, Posterior Sampling, Diffusion Models, Controlled Generation

10 A Simple Baseline for Multivariate Time Series Forecasting

链接：https://openreview.net/forum?id=oANkBaVci5

分数：5688

关键词：预测，小波变换

keywords：Time Series Forecasting, Wavelets

11 Shedding Light on Time Series Classification using Interpretability Gated Networks

链接：https://openreview.net/forum?id=n34taxF0TC

分数：56688

关键词：可解释性，Shapelet（特征提取）

keywords：Interpretability, Time-series, Shapelet

TL; DR： A framework to integrate interpretable models with deep neural networks for interpretable time-series classification.

12 Multi-Resolution Decomposable Diffusion Model for Non-Stationary Time Series Anomaly Detection

链接：https://openreview.net/forum?id=eWocmTQn7H

分数：6668

关键词：异常检测，多分辨率，扩散模型

keywords：Diffusion Model, Non-Stationary Time Series, Anomaly Detection, Multi-Resolution

TL; DR：This paper delves into the potential of multi-resolution technique and diffusion model for non-stationary time series anomaly detection, supported by rigorous mathematical proofs.

13 CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching

链接：https://openreview.net/forum?id=m08aK3xxdJ

分数：5668

关键词：异常检测，频域

keywords：Multivariate Time Series, Anomaly Detection

14 CoMRes: Semi-Supervised Time Series Forecasting Utilizing Consensus Promotion of Multi-Resolution

链接：https://openreview.net/forum?id=bRa4JLPzii

分数：5668

关键词：多尺度，半监督

keywords：Time series forecasting, Multi-scale, Semi-supervised learning

TL; DR：we propose a novel semi-supervised time series forecasting utilzing con

15 Towards Neural Scaling Laws for Time Series Foundation Models

链接：https://openreview.net/forum?id=uCqxDfLYrB

分数：5668

keywords：Time series, scaling law, foundation model, transformer, forecasting

16 Quantifying Past Error Matters: Conformal Inference for Time Series

链接：https://openreview.net/forum?id=RD9q5vEe1Q

分数：5668

关键词：不确定性量化，分布偏移

keywords：Time Series; Uncertainty Quantification; Conformal Prediction; Distribution Shift

17 TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation

链接：https://openreview.net/forum?id=MZDdTzN6Cy

分数：5668

关键词：卷积

keywords：Time series Analysis, Dynamic convolution, Deep Learning

TL; DR：New time series modeling perspective based 3D-variation and new analysis framework based dynamic convolution

18 In-context Time Series Predictor

链接：https://openreview.net/forum?id=dCcY2pyNIO

分数：3668

关键词：预测，上下文学习

keywords：Time Series Forecasting, In-context Learning, Transformer

19 Compositional simulation-based inference for time series

链接：https://openreview.net/forum?id=uClUUJk05H

分数：566668

关键词：贝叶斯推断

keywords：Simulation-based inference, Bayesian inference, time series, markovian simulators, Amortized Bayesian inference

20 Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

链接：https://openreview.net/forum?id=aKcd7ImG5e

分数：6666

关键词：异常检测

keywords：Time series, Anomaly detection

TL; DR：We propose a general time series anomaly detection model that is pre-trained on multi-domain datasets and can subsequently apply to many downstream scenarios

21 TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting

链接：https://openreview.net/forum?id=wTLc79YNbh

分数：3588

关键词：预测，KAN

keywords：kolmogorov-Arnold Network; Time Series Forecasting

22 Investigating Pattern Neurons in Urban Time Series Forecasting

链接：https://openreview.net/forum?id=a9vey6B54y

分数：6666

关键词：时空预测（更像是），城市时间序列预测模型

keywords：urban time series forecasting, neuron detection

23 Locally Connected Echo State Networks for Time Series Forecasting

链接：https://openreview.net/forum?id=KeRwLLwZaw

分数：6666

关键词：回声状态网络

keywords：Time Series Analysis, Time Series Forecasting, Recurrent Networks, Regression, Echo State Networks

TL; DR： Improved locally connected ESN method comparable with state-of-the-art on real-world time series datasets.

24 Diffusion-based Decoupled Deterministic and Uncertain Framework for Probabilistic Multivariate Time Series Forecasting

链接：https://openreview.net/forum?id=HdUkF1Qk7g

分数：6666

关键词：长时预测，扩散模型

keywords：long-term time series forecasting, deep learning, diffusion model

25 TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting

链接：https://openreview.net/forum?id=rDe9yQQYKt

分数：666

关键词：脉冲神经网络

keywords：spiking neural network, time series forecasting, Application

TL; DR：We proposed a Temporal Segment Spiking Neuron Network (TS-LIF) for multivariate time series forecasting, supported by stability analysis and frequency response analysis to demonstrate its effectiveness and efficiency.

26 Exploring Representations and Interventions in Time Series Foundation Models

链接：https://openreview.net/forum?id=IRL9wUiwab

分数：6666

keywords：Time Series Foundation Models, Model Steering, Interpretability, Pruning

TL; DR：We investigate why time series foundation models work, the kinds of concepts that these models learn, and how can these concepts be manipulated to influence their outputs?

27 FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction

链接：https://openreview.net/forum?id=9EiWIyJMNi

分数：556668

关键词：Mamba,FFT

keywords：Mamba; Time Series Prediction

28 KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Processes for Time Series Forecasting

链接：https://openreview.net/forum?id=5oSUgTzs8Y

分数：66666

keywords：Probabilistic time series prediction; Neural Process; Deep Koopman model

29 Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series

链接：https://openreview.net/forum?id=syC2764fPc

分数：6666

keywords：Time Series, Large Language Models, Context-Alignment

TL; DR：LLMs for time series tasks

30 TwinsFormer: Revisiting Inherent Dependencies via Two Interactive Components for Time Series Forecasting

链接：https://openreview.net/forum?id=BSsyY29bcl

分数：55568

keywords：Inherent Dependencies, Interactive Components, Time Series Forecasting

TL; DR：A novel Transformer-and decomposition-based framework using residual and interactive learning for time series forecasting.

31 DyCAST: Learning Dynamic Causal Structure from Time Series

链接：https://openreview.net/forum?id=WjDjem8mWE

分数：3668

关键词：

TL; DR：dynamic causal discovery; time series

32 Drift2Matrix: Kernel-Induced Self Representation for Concept Drift Adaptation in Co-evolving Time Series

链接：https://openreview.net/forum?id=prSJlvWrgE

分数：3866

TL; DR：co-evolving time series, concept drift, kernel representation learning

相关链接

ICLR 2025 OpenReview：https://openreview.net/group?id=ICLR.cc/2025/Conference#tab-active-submissions

ICLR 2025分数统计：https://papercopilot.com/statistics/iclr-statistics/iclr-2025-statistics/

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