Coder Social home page Coder Social logo

mds--lstm-stock-prices's Introduction

Predecir S&P 500 usando LSTM

Proyecto para predecir los precios de cierre del S&P500 utilizando Long Short Term Memory RNN en tensorflow.

Se uso un LSTM multivariado utilizando como features el precio de cierre, el volumen y el Chicago Board Options Exchange Market Volatility Index (VIX).

Se obtuvo un validation loss de 0.24 utilizando 64 neuronas.

mds--lstm-stock-prices's People

Contributors

fabianigual avatar

Stargazers

 avatar

Watchers

 avatar

Forkers

koi-boy

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.