Stock price forecasting stuff with BERT

In this repository, the goal is to predict the tick direction of a stock based on its current order book and trade data. A LSTM Neural Network is used as an example of potential solution for such problem.

A Django app to predict realtime stock market prices for NSE India and NYSE using LSTM.

Implementação de uma LSTM para prever fechamento de ações na B3. Scripts para análise e predição de valores.

Forecasting stock prices with Long Short Term Memory (LSTM) architecture

Analyzing and predicting Google's stock prices through detailed data exploration and advanced LSTM models. This project involves data preprocessing, creating time-series sequences, constructing and training LSTM networks, and evaluating their performance to forecast future stock prices utilizing Python and Machine Learning libraries.

Easy to follow stock price analysis on Indian stock data

A personal end-to-end machine learning project that seeks to predict stock prices from economical indicators for a variety of USA-based companies.

Backtesting software for intraday and daily timeframe SARIMAX forecasting model. Capable of forecasting on any asset class with backtesting capability across various parameter sets customizable by the user.

This repository contains code for implementing both Large Language Models (LLM) and Long Short-Term Memory (LSTM) models in AWS SageMaker Studio Lab. It includes notebooks for LLM-based applications and LSTM models for stock price prediction.

Deep learning project for forecasting Google stock prices using LSTM. Includes EDA, SMA insights, LSTM tuning, and model performance comparison.

A time series forecasting project using ARIMA, Prophet, and Stacked BiLSTM models to predict Apple stock prices. Includes technical indicators, Fourier transforms, and residual modeling for improved accuracy.

Implementation of Time Series

Stock Forecasting

Datasets for the OAMLS paper: Objective-Aware Meta-Learning System for Financial Time Series Forecasting.

Stock Price Forecasting using Python, yfinance and Facebook Prophet — pulled 753 days of live Apple (AAPL) stock data, calculated moving averages and volatility, predicted 30-day price direction with confidence intervals. Built with pandas, matplotlib and Prophet.

Progetto di tesi: rete LSTM multivariata con sentiment analysis per la previsione dei titoli in borsa

LLM-based time-series forecasting and stock forecasting experiments

Stock prediction model

Multi-model ML dashboard for short-horizon stock price forecasting with technical analysis and news sentiment.