202010

 

Keras 中的多变量时间预测-LSTMs

 

教你多变量时间序列预测模型LSTM

——将时间序列问题转换为监督学习问题；LSTM通过时间步进行反向传播

 

使用ARIMA进行时间序列预测 Prophet

为你推荐：用Python进行时间序列预测的7种方法

 

如何用 LSTMs做预测？博士带你学LSTM

 

LSTM model arch for rare event time series forecasting

 

TS event forecasting with NN at Uber

Deep and Confident Prediction for TS at Uber

Engineering Extreme Event Forecasting at Uber with RNN

Forecasting at Uber: an intro

Short term traffic forecasting: where we are and where we're going

deep learning for short-term traffic flow prediction 

 

Using GANs for generation of realistic city-scale ride sharing/hailing data sets

 

Forecasting Taxi Demand with FCN and Temporal Guided Embedding 

 

Two-stream Multi-Channel CNN for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

 

gated residual recurrent GNN for traffic prediction 

 

Modeling ST Dynamics for traffic prediction 

 

Structure-aware CNN

 

semi-supervised cls with GCN

...GCN的几篇经典文章 

 

 

 

如何优雅地使用 Jupyter？

 

Hive 学习内置条件和字符串函数

Hive 学习之内置聚合函数

lxw的大数据田地

 

徐凯：scala处理日志，使用匿名函数

 

Python Cooper 

 

Tableau：Christopher Stolte 

Polaris

Multiscale Visualization Using Data Cubes

 

 

Graph Neural Network Review by 吴天龙
推荐系统遇上深度学习

 

标签：Uber,Forecasting,forecasting,prediction,traffic,202010,LSTM
来源： https://www.cnblogs.com/cx2016/p/13785332.html