2021年三大顶会时间序列预测论文&代码整理

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2021年最新时间序列预测论文&代码整理

AAAI 2021

1. Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting

• 下载：https://arxiv.org/abs/2009.05135

• 代码：https://github.com/ostadabbas/DSARF

2. Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series

• 下载：https://arxiv.org/abs/2103.02164

• 代码：https://paperswithcode.com/paper/dynamic-gaussian-mixture-based-deep#code

3. Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting

• 下载：https://arxiv.org/abs/2101.10460

• 代码：未找到

4. Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

• 下载：https://arxiv.org/abs/2102.00431

• 代码：未找到

5. Correlative Channel-Aware Fusion for Multi-View Time Series Classification

• 下载：https://arxiv.org/abs/1911.11561

• 代码：未找到

6. Learnable Dynamic Temporal Pooling for Time Series Classification

• 下载：https://arxiv.org/abs/2104.02577

• 代码：https://github.com/donalee/DTW-Pool

7. ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17018

• 代码：未找到

8. Joint-Label Learning by Dual Augmentation for Time Series Classification

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17071

• 代码：未找到

9. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

• 下载：https://arxiv.org/abs/2106.06947

• 代码：https://github.com/d-ailin/GDN

10. Time Series Anomaly Detection with Multiresolution Ensemble Decoding

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17152

• 代码：未找到

11. Outlier Impact Characterization for Time Series Data

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17379

• 代码：未找到

12. Generative Semi-Supervised Learning for Multivariate Time Series Imputation

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17086

• 代码：https://githubmemory.com/repo/zjuwuyy-DL/Generative-Semi-supervised-Learning-for-Multivariate-Time-Series-Imputation

13. Bridging Towers of Multi-Task Learning with a Gating Mechanism for Aspect-Based Sentiment Analysis and Sequential Metaphor Identification

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17596

• 代码：未找到

14. C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer

• 下载：https://arxiv.org/abs/2012.08976

• 代码：https://github.com/wswdx/C2F-FWN

15. Inductive Graph Neural Networks for Spatiotemporal Kriging

• 下载：https://arxiv.org/abs/2006.07527

• 代码：https://github.com/Kaimaoge/IGNNK

16. Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/17329

• 代码：https://github.com/zbs881314/Temporal-Coded-Deep-SNN

17. Continuous-Time Attention for Sequential Learning

• 下载：https://ojs.aaai.org/index.php/AAAI/article/view/16875

• 代码：未找到

18. ChronoR: Rotation Based Temporal Knowledge Graph Embedding

• 下载：https://arxiv.org/abs/2103.10379

• 代码：未找到

19. Learning from History: Modeling Temporal Knowledge Graphs with Sequential CopyGeneration Networks

• 下载：https://arxiv.org/abs/2012.08492

• 代码：未找到

20. Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

• 下载：https://arxiv.org/abs/1911.11455

• 代码：未找到

ICML 2021

1. Voice2Series: Reprogramming Acoustic Models for Time Series Classification

• 下载：https://arxiv.org/abs/2106.09296

• 代码：https://github.com/huckiyang/Voice2Series-Reprogramming

2. Neural Rough Differential Equations for Long Time Series

• 下载：https://arxiv.org/abs/2009.08295

• 代码：https://github.com/jambo6/neuralRDEs

3. Necessary and sufficient conditions for causal feature selection in time series with latent common causes

• 下载：https://arxiv.org/abs/2005.08543

• 代码：未找到

4. Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

• 下载：https://arxiv.org/abs/2101.12072

• 代码：未找到

5. Conformal prediction interval for dynamic time-series

• 下载：https://arxiv.org/abs/2010.09107

• 代码：https://github.com/hamrel-cxu/EnbPI

6. Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

• 下载：https://arxiv.org/abs/2105.04100

• 代码：https://github.com/Z-GCNETs/Z-GCNETs

7. End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

• 下载：https://proceedings.mlr.press/v139/rangapuram21a.html

• 代码：https://github.com/awslabs/gluon-ts

8. Approximation Theory of Convolutional Architectures for Time Series Modelling

• 下载：http://proceedings.mlr.press/v139/jiang21d/jiang21d.pdf

• 代码：未找到

9. Whittle Networks: A Deep Likelihood Model for Time Series

• 下载：http://proceedings.mlr.press/v139/yu21c.html

• 代码：https://github.com/ml-research/WhittleNetworks

10. Explaining Time Series Predictions with Dynamic Masks

• 下载：https://arxiv.org/abs/2106.05303

• 代码：https://github.com/JonathanCrabbe/Dynamask

11. ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer

- 下载：https://arxiv.org/pdf/2107.05887.pdf

- 代码：未找到

12. Temporal Dependencies in Feature Importance for Time Series Predictions

• 下载：https://arxiv.org/abs/2107.14317

• 代码：未找到

IJCAI

1. Time-Aware Multi-Scale RNNs for Time Series Modeling

• 下载：https://www.ijcai.org/proceedings/2021/315

• 代码：https://github.com/qianlima-lab/TAMS-RNNs

2. Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

• 下载：https://www.ijcai.org/proceedings/2021/397

• 代码：未找到

3. TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data

• 下载：https://arxiv.org/abs/2105.00412

• 代码：未找到

4. Time-Series Representation Learning via Temporal and Contextual Contrasting

• 下载：https://arxiv.org/abs/2106.14112

• 代码：https://github.com/emadeldeen24/TS-TCC

5. Time Series Data Augmentation for Deep Learning: A Survey

• 下载：https://arxiv.org/abs/2002.12478

• 代码：无

6. Uncertain Time Series Classification

• 下载：https://www.ijcai.org/proceedings/2021/0683.pdf

• 代码：https://github.com/frankl1/ustc

7. Learning Temporal Causal Sequence Relationships from Real-Time Time-

• 下载：https://arxiv.org/abs/1905.12262

• 代码：未找到

8. Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation

• 下载：https://www.ijcai.org/proceedings/2021/378

• 代码：https://github.com/jarheadjoe/Adv-spec-ker-matching

9. Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling

• 下载：https://www.ijcai.org/proceedings/2021/493

• 代码：未找到

参考文献

1. https://www.yanxishe.com/reportDetail/26029

2. https://icml.cc/Conferences/2021/Schedule?type=Poster

3. https://ijcai-21.org/program-main-track/

4. https://dreamhomes.top/posts/202108241839

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