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sqlr13

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

stock-market-prediction-machine-learning

—Machine Learning; stock prediction; Deep Learning; styling; LSTM(Long Short Term Memory)

stock-predection

Stock Prediction using machine learning

stock-prediction-models

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

stockprediction

Plain Stock Close-Price Prediction via Graves LSTM RNNs

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.

stockpredictionrnn

High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

stockpriceprediction

Stock Price Prediction using Machine Learning Techniques

test-stock-prediction-algorithms

Use deep learning, genetic programming and other methods to predict stock and market movements

time-series-forecasting-with-wavelets

value-investing-studies

Data Analysis Studies on Value Investing

weather_prediction

This is the weather prediction machine learning model which ll help to predict different parameter of weather like, Temperature, wind speed, air pollution index.

web-traffic-forecasting

Kaggle | Web Traffic Forecasting 📈

wind-energy-prediction-using-lstm

Time Series Analysis using LSTM for Wind Energy Prediction.

wind_turbine_predictions

windml

The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. Renewable Wind Energy, Forecasting, Prediction

windpowerlib

windturbineclassification

Some Python codes from my master's thesis on wind turbine fault prediction using machine learning

workshop-ml-finance

git repo for the Machine Learning in Finance of Applied Machine Learning Days 2019

wpionic

Ionic app with WordPress integration

yamm

Yet Another Matlab MCMC code