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.

Pull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework

An investment portfolio of stocks is created using Long Short-Term Memory (LSTM) stock price prediction and optimized weights. The performance of this portfolio is better compared to an equally weighted portfolio and a market capitalization-weighted portfolio.

Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory) then compare make Inferences about the model.

Price prediction for Crypto, Stock, and Index using Hybrid Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) models on PyTorch.

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.

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

This project seeks to utilize Deep Learning models, LongShort Term Memory (LSTM) Neural Network algorithm to predict stock prices. We will use Keras to build a LSTM RNN to predict stock prices using historical closing price and trading volume and visualize both the predicted price values over time and the optimal parameters for the model.

An Interactive Financial Dashboard for investors who are interested in obtaining stock insights of various companies including various financial institution stocks. Power BI Business Analytics is the platform used to present the Visualization of the data. The Dashboard allow investors to interactively analyze various stocks prices to current market Machine Learning for price prediction using Facebook Prophet and Long Short Term Memory models

Stock market prediction is an attempt of determining the future value of a stock traded on a stock exchange. This project focuses on classification problems, predicting the next-second price movement, and acting upon the insights generated from our models. We implemented multiple machine learning algorithms including logistic regression, support vector machines (SVM), Long- Short Term Memory (LSTM), and Convolutional Neural Networks (CNN) to determine the trading action in the next minute. Using the predicted results from our models to generate the portfolio value over time, the support vector machine with a polynomial kernel performs the best among all of our models.

in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predicting stock prices.

This project is an interactive web application that uses a Long Short-Term Memory (LSTM) deep learning model to predict stock prices. It features automated hyperparameter optimization with Keras Tuner and a real-time dashboard built with Streamlit for visualizing predictions against live data from Yahoo Finance.

Predicting the trend in the stock price is very complicated and challenging job as it depends on various factors or on different variables like economic conditions, investors’ expectations, and the political events. Because of the different variables, it is necessary to estimate the value of the product correctly and to obtain scoring income. One of the common subjects in the financial domain was stock-market prediction. A lot of mathematical models were employed before progressing in machine learning and artificial intelligence. While these models provided fairly accurate results, using these models was not effective and time consuming due to the rapidly evolving nature of the consumer. As a result of developments in machine learning and artificial intelligence, it has become possible to employ different models such as neural networks, regression, decision tree, Bayesian networks etc. Long Short-term Memory is used in this paper to predict the the stock price of the selected company. All the work and algorithm discussed in this paper will be done on Jupyter notebook which is free available Python based that enables quantitative analyst to prepare their own trading strategy.

This project predicts stock prices using a Long Short-Term Memory (LSTM) neural network. It fetches historical stock data using Yahoo Finance API, preprocesses data for training, trains an LSTM model, a recurrent neural network (RNN), for prediction, and visualizes actual vs. predicted stock prices.

Stock Price Prediction Using Recurrent Neural Network and Long short-term memory(LSTM) is build for real time prediction and monitoring of the stock prices for any company based on its previous performance. This module requires the use of Keras, Pandas and Matplotlib Libraries of Python for its implementation

A deep learning-based stock price prediction application using Long Short-Term Memory (LSTM) neural networks and Flask API.

Leverages Long Short-Term Memory (LSTM) neural networks to forecast stock prices using historical data. This project showcases how deep learning can be applied to time series analysis for technical stock market prediction, using one year of data from TataGlobal (NSE) as a case study.

Edelweiss is an AI-based stock price prediction system that uses LSTM (Long Short-Term Memory) models to learn patterns from historical stock data and predict future price trends.

A highly flexible deep learning model for stock price prediction using Long Short-Term Memory (LSTM) networks with an attention mechanism. Stock-agnostic, it captures long-range dependencies in time-series data while prioritizing key historical patterns for improved predictive accuracy, making it adaptable to various stocks and market conditions.

AAPL stock price prediction using long short term memory(recurrent neural network) using pytorch

Performing stock price prediction using Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) models on historical stock data.

iRobot Co. Stock Prediction is a Machine Learning program using Long Short-Term Memory network to predict IRBT stock price for the next coming months

A project focused on forecasting Microsoft stock prices using Long Short-Term Memory (LSTM) neural networks. Includes a dataset (MSFT.csv) and a Jupyter Notebook (Forecasting Microsoft Stock Prices Using LSTMs.ipynb) for preprocessing, model training, and prediction visualization.

A stock price prediction model using Long Short-Term Memory (LSTM) neural networks combined with sentiment analysis of financial news articles. Developed using Python, Used TensorFlow, NLTK, and various data science libraries.

In this project, I ventured into the exciting world of time series analysis and deep learning to build a robust Stock Price Prediction model using Long Short-Term Memory (LSTM) networks.

This project predicts stock prices using an LSTM (Long Short-Term Memory) model trained on historical stock data. Users can select a company and get future price predictions for the next 7 days, along with profit/loss calculations and visualization graphs.

Stock Prices Prediction using Deep Learning Models. Financial markets have a vital role in the development of modern society. ... Long-short term memory (LSTM) is then used to predict the stock price. The prices, indices and macroeconomic variables in past are the features used to predict the next day's price

Stock Trend Prediction is a project that leverages Long Short-Term Memory (LSTM) neural networks to predict future stock prices using historical data. By integrating advanced machine learning techniques with a user-friendly web interface, it provides insights into stock trends and forecasts.

Real-time stock prediction analysis using LSTM is a machine learning technique that is used to forecast future stock prices based on historical data. LSTM stands for Long Short-Term Memory, which is a type of recurrent neural network that is well-suited for processing sequential data, such as stock prices.

Alpha Trend AI is a deep learning-powered tool designed to predict stock price trends based on historical data using LSTM (Long Short-Term Memory) networks. It provides an intuitive, interactive dashboard for visualizing predictions and exploring financial indicators like Moving Averages and RSI.