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

Plain Stock Close-Price Prediction via Graves LSTM RNNs

used for Stock Prodiction&power prediction&Traffic prediction by ARIMA,xgboost,RNN,LSTM,TCN

BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!

LSTM RNN for sentiment-based stock prediction

Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

RNN - Stock Prediction Model using Attention Multilayer Recurrent Neural Networks with LSTM Cells

Educational predictions on stock market with Tensorflow.js sequential RNN with LSTM layers on a React web App.

Building a LSTM RNN Model for Stock Prediction using Tensorflow/Keras

This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture. This is for single stock prediction and backtesting, another RNN LSTM network and backtester for multiple-stock portfolio will be added soon.

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

(1) LSTM-RNN stock prices (historical closing precies of S&P500) prediction using keras with tensorflow. (2) Experiments APIs on the network's hyper-parameters are provided through './mmodel/experiment.py'. (3) a website is built using this prediction model as engine with Flask and MySQL.

Designing a Machine Learning algorithm to predict stock prices is a subject of interest for economists and machine learning practitioners. Financial modelling is a challenging task, not only from an analytical perspective but also from a psychological perspective. After 2008 financial crisis, many financial companies and investors shifted their interest towards predicting future trends. Most of the existing methods for stock price forecasting are modelled using non-linear methods and evaluated on specific data sets. These models are not able to generalize for diverse datasets. Financial time series data is highly dynamic in nature and makes it difficult to analyze through statistical methods. Recurrent Neural Networks (RNN) based Long Short- Term Memory (LSTM) networks were able to capture the patterns of the sequences data meanwhile statistical methods tried to generalize by memorizing data instead of recognizing patterns. In this work, we examined the performance of LSTM model and statistical models over stock prices of different companies to generalize the model. The experimental results of this study show that, LSTM network outperformed traditional statistical methods like ARIMA, MA and AR models. Furthermore, we have noticed that, LSTM network was able to perform consistently on different data sets while statistical methods showed varied performance. Through this project, we addressed the gaps in current models of stock price prediction in both economic and machine learning perspective.

This repository will consist of a Long Short-Term Memory implementation of a Recurrent Neural Network used to predict the stock prices of Google Stocks for the next working day based on their past few days opening price trends.

Google Stock Price Prediction using Long Short-Term Memory (LSTM) is a deep learning-based approach to forecasting stock prices using historical data. LSTM is a type of recurrent neural network (RNN) that is well-suited for sequential data like stock prices

Google Stock Price Prediction Using RNN - LSTM

Stock price prediction implemented with Flask, tensorflow 2.0 using LSTM RNN.

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

Created a real-time stock prediction platform in Flask, React, WebSockets, and Next.js, using ARIMA, LSTM, RNN and CNN models, with real-time data streaming via Apache Kafka and Apache Flink. Deployed Dockerized app and model services on AWS EKS with Kubernetes and set up Grafana monitoring

Stock prediction done using RNN and LTSM to resolve vanishing gradient problem. Dataset used is obtained from Pakistan Stock Exchange

Developed an enhanced stock price forecasting solution using YFinance with LSTM and RNN, for improved prediction accuracy.

LSTM-based recurrent neural network which trains RNN on 30-day span of stock data, then accepts 30-day span to make prediction for the 31st day; inspired by the following paper: https://arxiv.org/pdf/1603.07893.pdf

This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

In this project we predicted the adjusted closing price of the stocks using techniques, regression analysis using XGBoost and hyper-parameter tuning of the same for better accuracy. Achieved final RMSE metric of 33.59 and MAPE of 1.552%.

Using historical financial data and aggregated social media sentiment from Twitter to determine the stock signal (Buy or Sell) for a given trading day. Comparing the performance of deep learning models such as LSTMs, GRUs, and RNNs when performing stock signal prediction with and without social media sentiments

Prediction of Stock price using Recurrent Neural Network (RNN) models. Contains GRU, LSTM, Bidirection LSTM & LSTM combinations with GRU units. The models were deveoped using the keras module from Tensorlfow.

The main content of this source code includes: 1. Wavelet denoising of investment factors. 2. Stock price trend prediction using LSTM, GRU, RNN, BPNN, and SVR algorithms based on investment factors. 3. Constructing a quantitative investment strategy and conducting backtesting based on the prediction results.

Stock Market Prediction using Deep Learning and Markov Chain Models The models is combination of RNN ( Recurrent Neural Network ), MCM’s( Markov Chain Models ), LSTM( Long Short Term Memory)

RNN, LSTM, Multi-layers, stock trend prediction

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