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attentioned-dual-stage-stock-prediction

By PyTorch

baidutraffic

This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).

bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

dcrnn

Implementation of Diffusion Convolutional Recurrent Neural Network

deep-convolutional-residual-lstm

deep-forecast

The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.

deep-forecast-pytorch

Wind Speed Prediction using LSTMs in PyTorch (https://arxiv.org/pdf/1707.08110.pdf)

deep-learning-har

Convolutional and LSTM networks to classify human activity

deepconvlstm

Deep learning framework for wearable activity recognition based on convolutional and LSTM recurretn layers

deeplearning-time-series

LSTM for time series forecasting

deepst

Deep Learning for Spatio-Temporal Data

deepwalk

DeepWalk - Deep Learning for Graphs

dmvst-net

DMVST-Net for AAAI 2018

dsgc

firefly-proxy

A proxy software to help circumventing the Great Firewall.

free-programming-books-zh_cn

:books: 免费的计算机编程类中文书籍，欢迎投稿

geoman

A deep learning approach for spatio-temporal forecasting

gitskills

gnnpapers

Must-read papers on graph neural networks (GNN)

htsprophet

Hierarchical Time Series Forecasting using Prophet

kaggle-web-traffic

1st place solution

keras-tensorflow-financial-time-series-signal-forecast

Financial Time Series Price forecast using Keras for Tensorflow. RNN LSTM

learngit

lstnet

mapbj

multivariate-time-series-forecast-using-seq2seq-in-tensorflow

nnet-ts

Neural network architecture for time series forecasting.

predict-new-york-taxi-demand

The project focuses on predicting the number of taxi pickups given a one-hour time window and a location within New York City. - See more at: http://blog.nycdatascience.com/student-works/predict-new-york-city-taxi-demand/#sthash.TaA8lkLJ.dpuf

rmn

Contains CNTK recipe for the "residual memory networks" as in paper https://www.researchgate.net/publication/311768079_Residual_Memory_Networks_Feed-forward_approach_to_learn_long-term_temporal_dependencies . The ndl files are updated as we find improvements.

rnn-lstm-nyc-taxi