Repository: xihao-1223/TrafficFlowPredictionResources Branch: master Commit: 87d465bb97d8 Files: 2 Total size: 9.3 KB Directory structure: gitextract_au9p_xo0/ ├── LICENSE └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2020 btm1229 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ 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)
gitextract_au9p_xo0/ ├── LICENSE └── README.md
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (11K chars).
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