https://github.com/zzw-zwzhang/awesome-of-time-series-prediction

A curated list of time series prediction resources.
https://github.com/zzw-zwzhang/awesome-of-time-series-prediction
Last synced: 12 days ago
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A curated list of time series prediction resources.
Host: GitHub
URL: https://github.com/zzw-zwzhang/awesome-of-time-series-prediction
Owner: zzw-zwzhang
Created: 2021-10-30T08:29:20.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-12-04T10:09:39.000Z (about 3 years ago)
Last Synced: 2025-02-04T06:01:39.360Z (17 days ago)
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Size: 3.17 MB
Stars: 53
Watchers: 4
Forks: 9
Open Issues: 0
Metadata Files:
- Readme: README.md
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ultimate-awesome - awesome-of-time-series-prediction - A curated list of time series prediction resources. (Other Lists / Julia Lists)
README

        # Awesome-of-Time-Series-Prediction [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

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A curated list of time series prediction and related resources.

Please feel free to pull requests or open an issue to add papers.

### :high_brightness: Updated 2021-11-03

## Table of Contents

- [Type of Time Series Prediction](#type-oof-time-series-prediction)

- [Transformer-based Methods](#transformer-based)

- [Graph-based Methods](#graph-based)

- [Time Series Prediction](#time-series-prediction)

  - [2021 Venues](#2021)

  - [2020 Venues](#2020)

  - [2019 Venues](#2019)

  - [2018 Venues](#2018)

  - [2017 Venues](#2017)

  - [2016 Venues](#2016)

  - [Previous Venues](#2010-2014)

  - [arXiv](#arxiv)

 

- [Awesome Surveys](#awesome-surveys)

- [Awesome Blogs](#awesome-blogs)

- [Awesome Datasets](#awesome-datasets)

- [Awesome Code](#awesome-code)

### Type of Time Series Prediction

| Type        | `S`          | `MS`           | `Other-driven`                   | `Multi Modal`                 | ``                  | ``              | `Other`     |

|:----------- |:-------------:|:--------------:|:----------------------: |:---------------------:|:----------------------:|:-----------------:|:-----------:|

| Explanation | Stock | Multi-Stock | OD | MM |  |  | other types |

### Transformer-based Methods

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### Graph-based Methods

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### 2021

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction](https://www.ijcai.org/proceedings/2021/0508.pdf) | IJCAI | ``     | [PyTorch(Author)]()   |  ``  |

| [Bayesian Temporal Factorization for Multidimensional Time Series Prediction](https://arxiv.org/pdf/1910.06366.pdf) | TPAMI | ``     | [PyTorch(Author)]()   |  ``  |

| [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://arxiv.org/pdf/2101.06861.pdf) | ICLR | `Other`     | [PyTorch(Author)](https://github.com/chaoshangcs/GTS)   |  `67`  |

| [A Study of Joint Graph Inference and Forecasting](https://arxiv.org/pdf/2109.04979.pdf) | ICML-W | ``     | [PyTorch(Author)]()   |  ``  |

| [REST: Relational Event-driven Stock Trend Forecasting](https://arxiv.org/pdf/2102.07372.pdf) | WWW | `MS & OD`     | [PyTorch(Author)]()   |  ``  |

| [Financial time series forecasting with multi-modality graph neural network](https://www.sciencedirect.com/science/article/abs/pii/S003132032100399X) | PR | `MM`     | [PyTorch(Author)]()   |  ``  |

| [End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series](http://proceedings.mlr.press/v139/rangapuram21a/rangapuram21a.pdf) | ICML | ``     | [MexNet(Author)](https://github.com/rshyamsundar/gluonts-hierarchical-ICML-2021)   |  `9`  |

| [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/pdf/2012.07436.pdf) | AAAI-Best | `Other`     | [PyTorch(Author)](https://github.com/zhouhaoyi/Informer2020)   |  `1.8k`  |

| [Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks](https://ojs.aaai.org/index.php/AAAI/article/view/16077) | AAAI | `OD`     | [PyTorch(Author)](https://github.com/RuichengFIC/ADGAT)   |  `26`  |

| [ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3447548.3467330) | KDD | `MS`     | [PyTorch(Author)](https://github.com/JLDeng/ST-Norm)   |  `15`  |

| [Adjusting for Autocorrelated Errors in Neural Networks for Time Series](https://arxiv.org/pdf/2101.12578.pdf) | NeurIPS | ``     | [PyTorch(Author)](https://github.com/Daikon-Sun/AdjustAutocorrelation)   |  `8`  |

### 2020

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations](https://aclanthology.org/2020.emnlp-main.676.pdf) | EMNLP | `OD`     | [PyTorch(Author)](https://github.com/midas-research/man-sf-emnlp)   |  `6`  |

| [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://arxiv.org/pdf/2005.04955.pdf) | ICPR | `OD`     | [PyTorch(Author)](https://github.com/start2020/Multi-GCGRU)   |  `4`  |

| [Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction](https://www.ijcai.org/proceedings/2020/0640.pdf) | IJCAI | ``     | [PyTorch(Author)]()   |  ``  |

| [Multi-scale Two-way Deep Neural Network for Stock Trend Prediction](https://www.ijcai.org/proceedings/2020/0628.pdf) | IJCAI-FinTech | ``     | [PyTorch(Author)](https://github.com/marscrazy/MTDNN)   |  `30`  |

| [Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction](https://www.ijcai.org/proceedings/2020/0626.pdf) | IJCAI-FinTech | `MS`     | [PyTorch(Author)](https://github.com/liweitj47/overnight-stock-movement-prediction)   |  `17`  |

| [A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis](https://www.ijcai.org/proceedings/2020/0614.pdf) | IJCAI-FinTech | `MS`     | [PyTorch(Author)]()   |  ``  |

| [Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting](https://github.com/zzw-zwzhang/Awesome-of-Time-Series-Prediction/blob/main/paper/2020%20WWW__Domain%20Adaptive%20Multi-Modality%20Neural%20Attention%20Network%20for%20Financial%20Forecasting.pdf) | WWW | ``     | [PyTorch(Author)]()   |  ``  |

| [HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction](https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5211&context=eispapers1) | WWW | `OD`     | [PyTorch(Author)](https://github.com/YangLinyi/HTML-Hierarchical-Transformer-based-Multi-task-Learning-for-Volatility-Prediction)   |  `25`  |

| [Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations](https://aclanthology.org/2020.emnlp-main.676.pdf) | EMNLP | ``     | [PyTorch(3rd)](https://github.com/pigman0315/MAN-SF)   |  `0`  |

| [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://arxiv.org/pdf/2103.07719.pdf) | NeurIPS | `Other`     | [PyTorch(Author)](https://github.com/microsoft/StemGNN/)   |  `150`  |

| [Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values](https://arxiv.org/pdf/1911.10273.pdf) | AAAI | `MS`     | [PyTorch(Author)]()   |  ``  |

| [Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series ](https://ojs.aaai.org/index.php/AAAI/article/view/5496) | AAAI | `MS`     | [PyTorch(Author)](https://github.com/DerronXu/DeepTrends)   |  `41`  |

| [Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://arxiv.org/pdf/2002.12135.pdf) | AAAI | `MS`     | [PyTorch(Author)](https://github.com/huawei-noah/BHT-ARIMA)   |  `63`  |

| [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/pdf/2005.11650.pdf) | KDD | `MS`     | [PyTorch(Author)](https://github.com/nnzhan/MTGNN)   |  `314`  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### 2019

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction](https://songdj.github.io/publication/kdd-19-b/kdd-19-b.pdf) | KDD | `MS`     | [PyTorch(Author)]()   |  ``  |

| [Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction](https://www.microsoft.com/en-us/research/uploads/prod/2019/11/p2376-chen.pdf) | KDD | ``     | [PyTorch(Author)]()   |  ``  |

| [Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding](https://www.microsoft.com/en-us/research/uploads/prod/2019/11/p894-li.pdf) | KDD | ``     | [PyTorch(Author)]()   |  ``  |

| [Transformer-Based Capsule Network For Stock Movements Prediction](https://aclanthology.org/W19-5511.pdf) | IJCAI-FinNLP | ``     | [PyTorch(Author)]()   |  ``  |

| [Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network](https://aura.abdn.ac.uk/bitstream/handle/2164/12473/p678_deng.pdf;jsessionid=607F251CC0FA36201363178781DE3005?sequence=1) | WWW | `OD`     | [PyTorch(Author)]()   |  ``  |

| [Enhancing Stock Movement Prediction with Adversarial Training](https://www.ijcai.org/proceedings/2019/0810.pdf) | IJCAI | ``     | [TensorFlow(Author)](https://github.com/fulifeng/Adv-ALSTM)   |  `85`  |

| [CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets](https://www.ijcai.org/proceedings/2019/0514.pdf) | IJCAI | ``     | [PyTorch(Author)]()   |  ``  |

| [What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues](https://aclanthology.org/P19-1038.pdf) | ACL | `MM`     | [PyTorch(Author)](https://github.com/GeminiLn/EarningsCall_Dataset)   |  `52`  |

| [Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis](https://arxiv.org/pdf/1909.10660.pdf) | NeurIPS-W | ``     | [PyTorch(Author)]()   |  ``  |

| [Temporal Relational Ranking for Stock Prediction](https://arxiv.org/pdf/1809.09441.pdf) | ACM-TIS | `OD`     | [TensorFlow(Author)](https://github.com/fulifeng/Temporal_Relational_Stock_Ranking)   |  `176`  |

| [A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning](https://www.mdpi.com/2076-3417/9/22/4745/htm) | AS | `MS`     | [PyTorch(Author)]()   |  ``  |

| [DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News](https://arxiv.org/pdf/1912.10806.pdf) | NeurIPS | `OD`     | [PyTorch(Author)]()   |  ``  |

| [Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting](https://ojs.aaai.org//index.php/AAAI/article/view/4383) | AAAI | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### 2018

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Stock Movement Prediction from Tweets and Historical Prices](https://aclanthology.org/P18-1183.pdf) | ACL | `OD`     | [PyTorch(Author)](https://github.com/yumoxu/stocknet-dataset)   |  `267`  |

| [Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis](https://arxiv.org/pdf/1712.00975.pdf) | TNNLS | ``     | [PyTorch(Author)](https://github.com/viebboy/TABL)   |  `24`  |

| [Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction](http://www.sdspeople.fudan.edu.cn/zywei/paper/chen-cikm2018.pdf) | CIKM | `MS`     | [PyTorch(Author)]()   |  ``  |

| [Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News]() | CIKM | `OD`     | [PyTorch(Author)]()   |  ``  |

| [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://www.researchgate.net/publication/321604413_Listening_to_Chaotic_Whispers_A_Deep_Learning_Framework_for_News-oriented_Stock_Trend_Prediction) | WSDM | `OD`     | [PyTorch(Author)](https://github.com/donghyeonk/han)   |  `12`  |

| [A Memory-Network Based Solution for Multivariate Time-Series Forecasting](https://arxiv.org/pdf/1809.02105.pdf) | AAAI | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### 2017

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://arxiv.org/pdf/1704.02971.pdf) | IJCAI | ``     | [Keras(Author)](https://github.com/chensvm/A-Dual-Stage-Attention-Based-Recurrent-Neural-Network-for-Time-Series-Prediction)   |  `91`  |

| [Stock Price Prediction via Discovering Multi-Frequency Trading Patterns](https://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf) | KDD | ``     | [Keras(Author)](https://github.com/z331565360/State-Frequency-Memory-stock-prediction)   |  `54`  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

| []() | NeurIPS | ``     | [PyTorch(Author)]()   |  ``  |

### 2016

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Knowledge-Driven Event Embedding for Stock Prediction](https://aclanthology.org/C16-1201.pdf) | COLING | ``     | [PyTorch(Author)]()   |  ``  |

### Previous Venues

| Title    | Venue    | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Deep Learning for Event-Driven Stock Prediction](https://www.ijcai.org/Proceedings/15/Papers/329.pdf) | IJCAI | `OD`     | [PyTorch(Author)](https://github.com/vedic-partap/Event-Driven-Stock-Prediction-using-Deep-Learning)   |  `152`  |

| [Using Structured Events to Predict Stock Price Movement: An Empirical Investigation](https://emnlp2014.org/papers/pdf/EMNLP2014148.pdf) | EMNLP | `OD`     | [PyTorch(Author)]()   |  ``  |

### arXiv

| Title    | Date     | Type     | Code     | Star     |

|:-------- |:--------:|:--------:|:--------:|:--------:|

| [Multivariate Time Series Imputation by Graph Neural Networks](https://arxiv.org/pdf/2108.00298.pdf) | 2021.09.23 | `MS`     | -   |  ``  |

| [Artificial intelligence prediction of stock prices using social media](https://arxiv.org/pdf/2101.08986.pdf) | 2021.01.22 | `OD`     | -   |  ``  |

| [Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport]() | 2021.01.25 | `MS`     | -   |  ``  |

| [Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Adaptive Refined Labeling](https://arxiv.org/pdf/2107.11972.pdf) | 2021.01.26 | ``     | -   |  ``  |

| [Stock price prediction using BERT and GAN](https://arxiv.org/pdf/2107.09055.pdf) | 2021.01.28 | ``     | -   |  ``  |

| [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://arxiv.org/pdf/1908.07999.pdf) | 2019.11.12 | ``     | -   |  ``  |

| [Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks](https://arxiv.org/pdf/2107.00894.pdf) | 2021.07.02 | `Other`     | -   |  ``  |

| [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://arxiv.org/pdf/2104.05522.pdf) | 2021.04.23 | `Other`     | -   |  ``  |

| [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://arxiv.org/pdf/2005.04955.pdf) | 2020.10.26 | ``     | -   |  ``  |

| []() | 2021.01.28 | ``     | -   |  ``  |

| []() | 2021.01.28 | ``     | -   |  ``  |

## Awesome Repositories

- [deep-finance: Deep Learning for Finance](https://github.com/sangyx/deep-finance)

- [DL4Stock](https://github.com/jwwthu/DL4Stock)

- [Fintech Literature](https://github.com/ai-gamer/fintech-literature)

- [Deep learning models for traffic prediction](https://github.com/aptx1231/Traffic-Prediction-Open-Code-Summary)

- [Fintech-Survey: Paper & Code & Dataset Collection](https://github.com/NickHan-cs/Fintech-Survey)

- [FinNLP: Research sources for NLP in Finance](https://github.com/JLeeAI/FinNLP)

- [Graph-models-in-finance-application](https://github.com/jackieD14/Graph-models-in-finance-application)

- [GNN-finance: Curated list of Graph Neural Network Applications in Business and Finance](https://github.com/kyawlin/GNN-finance)

- [Deep Learning Time Series Forecasting](https://github.com/Alro10/deep-learning-time-series)

- [GNN4Fintech](https://github.com/jwwthu/GNN4Fintech)

- [Time-series-analysis-paper](https://github.com/snowaa007/Time-series-analysis-paper/blob/main/TimeSeries.md)

## Awesome Surveys

- [Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions](https://reader.elsevier.com/reader/sd/pii/S1566253520303481?token=C34A4FE9A3C52B54CA7A0B3D3EE29993944747A7C4AB5B82E75A9F994F84BB09B6582CE406BB0C72EE2173000EBE59B7&originRegion=us-east-1&originCreation=20211102032356), 2021 Information Fusion

- [Applications of deep learning in stock market prediction: Recent progress](https://www.sciencedirect.com/science/article/abs/pii/S0957417421009441), 2021 ESA

- [Financial time series forecasting with deep learning : A systematic literature review: 2005–2019](https://arxiv.org/pdf/1911.13288.pdf), 2019 ASC

- [Natural language based financial forecasting: a survey](https://dspace.mit.edu/bitstream/handle/1721.1/116314/10462_2017_9588_ReferencePDF.pdf?sequence=2&isAllowed=y), 2017

## Awesome Blogs

- [Stock predictions with state-of-the-art Transformer and Time Embeddings](https://towardsdatascience.com/stock-predictions-with-state-of-the-art-transformer-and-time-embeddings-3a4485237de6)

- [当深度学习遇上量化交易——因子挖掘篇](https://www.linkresearcher.com/theses/d27a60fc-3174-42a9-a1ed-1c05d44b3327)

- [Attention for time series forecasting and classification](https://towardsdatascience.com/attention-for-time-series-classification-and-forecasting-261723e0006d)

## Awesome Datasets

- [Top 5 Stock Market Datasets for Machine Learning](https://www.kaggle.com/learn-forum/167685)

- [Top 10 Stock Market Datasets for Machine Learning](https://imerit.net/blog/top-10-stock-market-datasets-for-machine-learning-all-pbm/)

## Awesome Code

- [Stock Prediction with Transformer (TensorFlow)](https://github.com/Stepka/Stock-Prediction-usning-Transformer-NN/blob/master/Stock_Prediction_usning_Transformer_NN.ipynb)

- [Transformer Time Series Prediction](https://github.com/oliverguhr/transformer-time-series-prediction)

- [DeepSeries: Deep Learning Models for time series prediction](https://github.com/EvilPsyCHo/Deep-Time-Series-Prediction)

- [Convolutional Recurrent Seq2seq GAN for the Imputation of Missing Values in Time Series Data](https://github.com/Tommy-Ngx/GAN_RNN_TS)
ecosyste.ms

Data

Tools

Indexes

Applications

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https://github.com/zzw-zwzhang/awesome-of-time-series-prediction

Awesome Lists containing this project

README
