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cogltx

deeplearningfortimeseriesforecasting

A tutorial demonstrating how to implement deep learning models for time series forecasting

drl

Deep Reinforcement Learning

fabric

Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.

fabric-samples

gluon-ts

Probabilistic time series modeling in Python

how-to-predict-stock-prices-easily-demo

How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

jupyterlab

JupyterLab computational environment.

kg-reeval

ACL 2020: A Re-evaluation of Knowledge Graph Completion Methods

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

lstnet

A Tensorflow / Keras implementation of "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks" paper

mlflow

Open source platform for the machine learning lifecycle

mm-wiki

MM-Wiki 一个轻量级的企业知识分享与团队协同软件，可用于快速构建企业 Wiki 和团队知识分享平台。部署方便，使用简单，帮助团队构建一个信息共享、文档管理的协作环境。

neo4j-kgbuilder

Neo4j+springboot+vue+d3.js知识图谱构建和可视化

pier

The Interchain gateway core implementation

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stock-rnn

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

stockprediction

Tools for getting and modeling Chinese stocks

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

test

time-series-forecasting

Recurrent Neural Network Implementations for Time Series Forecasting

tkgc

tkgcomplt

tpa-lstm

Temporal Pattern Attention for Multivariate Time Series Forecasting

vnpy

基于Python的开源量化交易平台开发框架

xiuno-docker

基于修罗/xiunoBBS的docker方案