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

**Repository Path**: wwdguu/Awesome-Deep-Learning-Based-Time-Series-Forecasting

## Basic Information

- **Project Name**: Awesome-Deep-Learning-Based-Time-Series-Forecasting
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-14
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Awesome-Deep-Learning-Based-Time-Series-Forecasting
## Time Series Forecasting Papers
### arxiv papers
#### 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
#### 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
#### 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
#### 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)
### AAAI
#### 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)
## Spatial-Temporal Time Series Forecasting Papers
### arxiv
- Deep forecast: Deep learning-based spatio-temporal forecasting (2017) [paper](https://arxiv.org/pdf/1707.08110.pdf)
- (AGCRN) Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020)[paper](https://arxiv.org/pdf/2007.02842.pdf) [code](https://github.com/LeiBAI/AGCRN)
### AAAI
#### 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)
### 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)
## 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)

## 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)