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└── README.md

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MIT License

Copyright (c) 2020 btm1229

Permission is hereby granted, free of charge, to any person obtaining a copy
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.


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FILE: README.md
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交通流量预测项目在研，以下是本人学习过程中积累整理的资源，会持续更新。

## PyTorch 资源

[资源汇总 awesomexx系列](https://github.com/INTERMT/Awesome-PyTorch-Chinese)  
[PyTorch中文文档](https://pytorch-cn.readthedocs.io/zh/latest/)

## 机器学习项目资源（竞赛、论文）

[论文 PaperWithCode](https://www.paperswithcode.com/methods)  
[论文 深度学习最新研究的论文预印版发表网站 ArXiV](https://arxiv.org/search/cs)  
[竞赛 Kaggle](https://www.kaggle.com/)  
[竞赛 DrivenData]( https://www.drivendata.org/)  
[竞赛 CrowdANALYTIX]( https://www.crowdanalytix.com/community)  
[竞赛 InnoCentive]( https://www.innocentive.com/our-solvers/)  
[竞赛 TundIT]( https://towardsdatascience.com/top-competitive-data-science-platforms-other-than-kaggle-2995e9dad93c)  
[竞赛 Codalab]( https://competitions.codalab.org/)  
[竞赛 Analytics Vidhya]( https://datahack.analyticsvidhya.com/)  
[竞赛 CrowdAI]( https://www.crowdai.org/challenges)  
[竞赛 Numerai]( https://numer.ai/rounds)  
[竞赛 Data Science Challenge]( https://www.datasciencechallenge.org/)  
[竞赛 KDD Cup]( https://www.kdd.org/kdd2019/kdd-cup)  
[竞赛 天池]( https://tianchi.aliyun.com/competition/gameList/activeList)  
[竞赛 腾讯广告算法大赛]( https://algo.qq.com)  

## 天池类似项目方案
[冠军 ppt](https://tianchi.aliyun.com/forum/postDetail?spm=5176.12586969.1002.15.573050b5r7Gsyv&postId=55137)    
[亚军1 ppt](https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.27.573050b5r7Gsyv&postId=54557)  
[亚军2 ppt](https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.30.573050b5r7Gsyv&postId=54550)  
[季军1 ppt](https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.18.573050b5r7Gsyv&postId=54574)  
[季军1 code](https://github.com/juzstu/TianChi_Hangzhou_Subway)  
[季军2 ppt 中期参考](https://tianchi.aliyun.com/forum/postDetail?spm=5176.12586969.1002.21.573050b5r7Gsyv&postId=54653)  
[季军3 ppt 中期参考](https://tianchi.aliyun.com/forum/postDetail?spm=5176.12586969.1002.24.573050b5r7Gsyv&postId=54599)  
[rank8 code](https://github.com/JanzYe/TianchiMetroFlow)  
[rank27 code](https://github.com/Hust-ZYD/SubwayFlowPredict?spm=5176.12282029.0.0.2bdd79b66UkH7X)  

[9.28更新-天池方案20+](https://github.com/Xiaoxiaohuangg/subway_traffic_forecast-tianchi/network/members)

## 其他类似项目方案

[Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU) code](https://github.com/xiaochus/TrafficFlowPrediction)  
[cnn_lstm code](https://github.com/bobbychovip/TrafficFlowPrediction)  
[交流流量预测应用方案收集 7篇论文 部分有code](https://github.com/xiaoxiong74/TrafficFlowForecasting)  
[交通流预测文献总结 里面有一些文献题目](https://github.com/dddssy/research_survey/tree/7d4b8b895515e498be3f22c4b3c1da0eaea29ef8)  
[pytorchts pytorch处理时序数据的封装 非官方 建议进阶时可以参考模型源码](https://github.com/zalandoresearch/pytorch-ts)  
[多属性时间序列预测](https://blog.csdn.net/weixin_41555408/article/details/107150046?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806454319725211944699%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806454319725211944699&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-7-107150046.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)  
[时间序列预测方法总结](https://blog.csdn.net/Poo_Chai/article/details/90924650?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806813219724842914414%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806813219724842914414&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-4-90924650.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)  

[9.28更新-交通预测PaperwithCode](https://paperswithcode.com/task/traffic-prediction)

## 特定模块资源整理
### 工程框架
[torch使用的基本套路 CSDN 较为简单](https://blog.csdn.net/qq_33431368/article/details/105826938?biz_id=102&utm_term=pytorch%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-1-105826938&spm=1018.2118.3001.4187)  
[基本代码框架参考 pytorch handbook](https://github.com/zergtant/pytorch-handbook)  

### 特征工程
[手把手教你用 Python 实现针对时间序列预测的特征选择](https://blog.csdn.net/weixin_34227447/article/details/90425314?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806454319725211944699%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806454319725211944699&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-1-90425314.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)
### 数据集构建
[pytorch构建多元时间序列数据集 Dataset](https://blog.csdn.net/itnerd/article/details/106265477?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806352019195265939941%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806352019195265939941&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-11-106265477.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)  
[DataLoader构建高效的自定义数据集](https://blog.csdn.net/zuiyishihefang/article/details/105985760?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806454319725211944699%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806454319725211944699&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-8-105985760.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)  
### 模型
[RNN 、LSTM、 GRU、Bi-LSTM 等常见循环网络结构以及其Pytorch实现](https://blog.csdn.net/sinat_34328764/article/details/104957897?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.edu_weight&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.edu_weight)  
[LSTM pytorch官方API](https://pytorch.org/docs/stable/nn.html?highlight=lstm#torch.nn.LSTM)  
[LSTM 官方案例详解](https://blog.csdn.net/wangwangstone/article/details/90296461?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159805964419724839215802%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=159805964419724839215802&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-5-90296461.pc_ecpm_v3_pc_rank_v3&utm_term=lstm+pytorch&spm=1018.2118.3001.4187)  
[LSTM 细节分析 CSDN](https://blog.csdn.net/shunaoxi2313/article/details/99843368?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159805964419724839215802%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=159805964419724839215802&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~top_click~default-1-99843368.pc_ecpm_v3_pc_rank_v3&utm_term=lstm+pytorch&spm=1018.2118.3001.4187)  
[LSTM进阶：使用LSTM进行多维多步的时间序列预测](https://blog.csdn.net/qq_35649669/article/details/89575949?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight)  
[LSTM 时间序列预测的一个小例子](https://www.jianshu.com/p/38df71cad1f6)  
[LSTM 实践多变量时间序列预测](https://blog.csdn.net/weixin_37665577/article/details/86584201?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159806748019724843333273%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159806748019724843333273&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v3-3-86584201.pc_ecpm_v3_pc_rank_v3&utm_term=pytorch+%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97&spm=1018.2118.3001.4187)  
[LSTM 客流预测](https://www.cnblogs.com/mathor/p/12416567.html#autoid-0-0-2)  
[LSTM 股价预测 支持三种框架](https://github.com/hichenway/stock_predict_with_LSTM)  
[GCN Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks 论文](https://www.researchgate.net/publication/333440253_Predicting_Station-Level_Short-Term_Passenger_Flow_in_a_Citywide_Metro_Network_Using_Spatiotemporal_Graph_Convolutional_Neural_Networks)

[TCN时序神经网络](https://www.cnblogs.com/PythonLearner/p/12925732.html)

### 算法

[一些算法的简明理解-知乎专栏](https://zhuanlan.zhihu.com/aitom)

[ Kalman-and-Bayesian-Filters-in-Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python)

[机器学习炼丹术-公众号-小白理解](https://www.cnblogs.com/PythonLearner/)

### 调参

[深度学习调参](https://blog.csdn.net/qq_20259459/article/details/70316511)