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https://github.com/fengyang95/Awesome-Deep-Learning-Based-Time-Series-Forecasting

List: Awesome-Deep-Learning-Based-Time-Series-Forecasting
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Host: GitHub
URL: https://github.com/fengyang95/Awesome-Deep-Learning-Based-Time-Series-Forecasting
Owner: fengyang95
Created: 2019-11-05T12:21:46.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2021-06-04T00:30:15.000Z (over 3 years ago)
Last Synced: 2024-05-23T06:02:30.416Z (7 months ago)
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Forks: 37
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-machine-learning-resources - **[List - Deep-Learning-Based-Time-Series-Forecasting?style=social) (Table of Contents)
README

        # Awesome-Deep-Learning-Based-Time-Series-Forecasting

## 1. Time Series Forecasting Papers

### Review

- Recurrent Neural Networks for Time Series Forecasting:Current status and future directions [paper](https://sci-hub.ren/https://www.sciencedirect.com/science/article/pii/S0169207020300996)

### arxiv 

#### 2019

- (DSTP-RNN) DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction [paper](https://arxiv.org/abs/1904.07464) [code](https://github.com/arleigh418/Paper-Implementation-DSTP-RNN-For-Stock-Prediction-Based-On-DA-RNN)

- (TPA-LSTM) Temporal Pattern Attention for Multivariate Time Series Forecasting [paper](https://arxiv.org/abs/1809.04206) [code](https://github.com/gantheory/TPA-LSTM)

- Foundations of sequence-to-sequence modeling for time series [paper](https://arxiv.org/pdf/1805.03714.pdf)

#### 2018

- (MTNet) A Memory-Network Based Solution for Multivariate Time-Series Forecasting [paper](https://arxiv.org/abs/1809.02105) [code](https://github.com/Maple728/MTNet)

- (HRHN) Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction [paper](https://arxiv.org/abs/1806.00685) [code](https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction)

- Conditional Time Series Forecasting with Convolutional Neural Networks [paper](https://arxiv.org/abs/1703.04691)

- A Multi-Horizon Quantile Recurrent Forecaster [paper](https://arxiv.org/pdf/1711.11053.pdf)

- EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction [paper](https://arxiv.org/pdf/1811.03760.pdf)

#### 2017

- DeepAR: Probabilistic forecasting with autoregressive recurrent networks [paper](https://arxiv.org/abs/1704.04110) [code](https://github.com/arrigonialberto86/deepar)

### NeurIPS

#### 2020

- Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting [paper](https://proceedings.neurips.cc/paper/2020/file/cdf6581cb7aca4b7e19ef136c6e601a5-Paper.pdf)

- Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting [paper](https://proceedings.neurips.cc/paper/2020/file/ce1aad92b939420fc17005e5461e6f48-Paper.pdf) [code](https://github.com/LeiBAI/AGCRN)

- Adversarial Sparse Transformer for Time Series Forecasting [paper](https://proceedings.neurips.cc/paper/2020/file/ce1aad92b939420fc17005e5461e6f48-Paper.pdf)

- Deep Rao-Blackwellised Particle Filters for Time Series Forecasting [paper](https://proceedings.neurips.cc/paper/2020/file/afb0b97df87090596ae7c503f60bb23f-Paper.pdf)

#### 2019

- (DILATE) Shape and Time Distorsion Loss for Training Deep Time Series Forecasting Models [paper](https://arxiv.org/abs/1909.09020) [code](https://github.com/vincent-leguen/DILATE) 

- Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting [paper](https://arxiv.org/abs/1905.03806)

- High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes [paper](https://arxiv.org/abs/1910.03002)

- Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting [paper](https://arxiv.org/abs/1907.00235)

#### 2018

- Deep State Space Models for Time Series Forecasting [paper](https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting.pdf)

#### 2017

### ICML

#### 2021

- Explaining Time Series Predictions With Dynamic Masks  

- Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting  

- Whittle Networks: A Deep Likelihood Model for Time Series  

- Neural Rough Differential Equations for Long Time Series  

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

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

#### 2019

- Deep Factors for Forecasting [paper](https://arxiv.org/pdf/1905.12417.pdf)

#### 2018

- Autoregressive Convolutional Neural Networks for Asynchronous Time Series [paper](https://arxiv.org/pdf/1703.04122.pdf)

- Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series [paper](http://proceedings.mlr.press/v80/che18a/che18a.pdf)

### SIGIR

#### 2018

- (LSTNet) Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks [paper](https://arxiv.org/abs/1703.07015) [code](https://github.com/laiguokun/LSTNet)

- A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic [paper](http://people.cs.pitt.edu/~milos/research/2018/SIGIR_18_Liu_Hierarchical_Seasonal_TS.pdf)

### SIGKDD

#### 2020

- Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403118) [code](https://github.com/nnzhan/MTGNN)

#### 2019

- Multi-Horizon Time Series Forecasting with Temporal Attention Learning [paper](https://www.kdd.org/kdd2019/accepted-papers/view/multi-horizon-time-series-forecasting-with-temporal-attention-learning)

#### 2021

- Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting [paper](https://arxiv.org/abs/2009.05135)  

- Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series [paper](https://arxiv.org/pdf/2103.02164.pdf)  

- Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting  [paper](https://www.aaai.org/AAAI21Papers/AAAI-3796.NguyenN.pdf)  

- Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [paper](https://arxiv.org/pdf/2102.00431.pdf)

#### 2019

- Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting [paper](https://www.aaai.org/ojs/index.php/AAAI/article/view/4383)

#### 2015

### IJCAI

#### 2019

- Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [paper](https://www.ijcai.org/proceedings/2019/402)

- Deep State Space Models for Time Series Forecasting [paper](https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting.pdf)

- Explainable Deep Neural Networks for Multivariate Time Series Predictions [paper](https://www.ijcai.org/proceedings/2019/0932.pdf)

#### 2018

- (GeoMAN) GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction [paper](https://www.ijcai.org/proceedings/2018/0476.pdf) [code](https://github.com/xchadesi/GeoMAN)

#### 2017

- (DA-RNN) A Dual-Stage Attention-Based Recurrent Neural Network

for Time Series Prediction [paper](https://www.ijcai.org/proceedings/2017/0366.pdf) [code](https://github.com/Zhenye-Na/DA-RNN)

### CIKM

#### 2019

- DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting [paper](https://kyonhuang.top/files/Huang-DSANet.pdf) [code](https://github.com/bighuang624/DSANet)

- Time Series Prediction with Interpretable Data Reconstruction [paper](http://www.cikm2019.net/attachments/papers/p2133-tianA.pdf)

### Others

- Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction [paper](https://www.sciencedirect.com/science/article/pii/S0168169919312499)

- Stock Price Prediction Using Attention-based Multi-Input LSTM [paper](http://proceedings.mlr.press/v95/li18c/li18c.pdf)

- Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction [paper](https://www.researchgate.net/publication/314202188_Co-evolutionary_multi-task_learning_with_predictive_recurrence_for_multi-step_chaotic_time_series_prediction)

- A New Timing Error Cost Function for Binary Time Series Prediction [paper](https://www.researchgate.net/publication/331033415_A_New_Timing_Error_Cost_Function_for_Binary_Time_Series_Prediction)

-  A bias and variance analysis for multistep-ahead time series forecasting [paper](https://www.researchgate.net/publication/274091015_A_Bias_and_Variance_Analysis_for_Multistep-Ahead_Time_Series_Forecasting)

## 2. Spatial-Temporal Time Series Forecasting Papers

### arxiv

#### 2020

#### 2019

- STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting [paper](https://arxiv.org/pdf/1905.10069.pdf) [code](https://github.com/LeiBAI/STG2Seq)

#### 2017

- Deep forecast: Deep learning-based spatio-temporal forecasting (2017) [paper](https://arxiv.org/pdf/1707.08110.pdf)

### AAAI

#### 2021

- (STFGNN) Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [paper](https://arxiv.org/pdf/2012.09641.pdf)

#### 2019

- (ASTGCN) Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting [paper](https://www.aaai.org/ojs/index.php/AAAI/article/view/3881) [code mxnet](https://github.com/Davidham3/ASTGCN)

- Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data [paper](https://aaai.org/ojs/index.php/AAAI/article/view/3841)

- Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting [paper](https://aaai.org/ojs/index.php/AAAI/article/view/3877)

- Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction [paper](https://arxiv.org/pdf/1803.01254.pdf)

- Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting [paper](http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdf)

#### 2018

- Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16069/15978)

- DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction  [paper](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16499/15759)

#### 2017

- Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction [paper](https://arxiv.org/pdf/1610.00081.pdf)  [code](https://github.com/lliony/DeepST-ResNet/tree/master/scripts/papers/AAAI17)

### IJCAI

#### 2019

- GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction [paper](https://www.ijcai.org/proceedings/2019/317)

#### 2018

- Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting [paper](https://www.ijcai.org/proceedings/2018/0505.pdf) [code-pytorch](https://github.com/FelixOpolka/STGCN-PyTorch)

### SIGKDD

#### 2020

- Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data [paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403358)

- AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction [paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403122)

## 3. Weather Forecasting Papers

### arxiv

### SIGKDD

#### 2019

- Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting [paper](https://arxiv.org/pdf/1805.03714.pdf) [code](https://github.com/BruceBinBoxing/Deep_Learning_Weather_Forecasting)

## 4. Vedio Prediction

### 2020 

- Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction (CVPR2020 PhyDNet) [paper](https://arxiv.org/abs/2003.01460) [code](https://github.com/vincent-leguen/PhyDNet)  

### 2019

- Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics (CVPR2019 MIM) [paper](https://arxiv.org/abs/1811.07490) [code](https://github.com/Yunbo426/MIM)

### 2018

- Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge [paper](https://arxiv.org/abs/1711.07970)  

- PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning (ICML2018) [paper](https://arxiv.org/abs/1804.06300) [code](https://github.com/Yunbo426/predrnn-pp)

### 2017

- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs (NIPS2017) [paper](https://papers.nips.cc/paper/6689-predrnn-recurrent-neural-networks-for-predictive-learning-using-spatiotemporal-lstms.pdf) [code](https://github.com/thuml/predrnn-pytorch)