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

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Stock price prediction with LSTMs in TensorFlow

Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).

Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot

A long term short term memory recurrent neural network to predict forex data time series

State of health (SOH) prediction for Lithium-ion batteries using regression and LSTM

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).

Time series prediction using LSTM classifier

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

LSTM based Vehicle Trajectory Prediction

This is an LSTM stock prediction using Tensorflow with Keras on top.

a implement of LSTM using Keras for time series prediction regression problem

Time Series Prediction with LSTM Using PyTorch

Use BPNN and LSTM to forecast stock price. 使用BP神经网络和LSTM预测股票价格，注释拉满。

remaining Useful Life (RUL) Prediction of Mechanical Bearings using Continuous Wavelet Transform (CWT), Convolution Neural Network (CNN), and Long Short Term Memory (LSTM) unit

e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond

Plain Stock Close-Price Prediction via Graves LSTM RNNs

Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测，注释拉满。

CIKM contest entry 'Convolutional LSTM neural network to extrapolate radar images, and predict rainfall'

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

Basic RNN, LSTM, GRU, and Attention for time-series prediction