The Quantum Counselor for portfolio investment is a tool with two main objectives: forecasting the trend of assets price and optimizing portfolio returns both using quantum computing techniques. For the case of the forecasting method, we use a hybrid method that combines a deep learning model of classical LSTM layers with quantum layers. For the case of portfolio optimization, the quantum algorithms of QAOA and VQE are used to solve the problem and will be compared with CPLEX, a classical solver. Both tools are deeply connected because the forecasted price of the different assets is used for the cost function construction.

Predict future stock prices using a pre-trained LSTM deep learning model. Upload a CSV file with historical stock data or use a sample to visualize trends and forecast closing prices.

One of the most critical application areas in the Financial Market especially sits on Stock Markets. In this area, the aim is trying to predict the future value of a specific stock by looking at its previous financial data on the exchange process in the market. In this paper, we proposed a system that uses a Deep Learning based approach for training and constructing a knowledge base on a specific stock such as "IBM". We get time series values of the stock from the New York Stock Exchange which starts from 1968 up to 2018. Experimental results showed that this approach produces very good forecasting for specific stocks.

Deep Learning Based Forecasting in Stock Market with Big Data Analytics

This project, in collaboration with the RAO Lab at EPFL, focuses on predicting Bitcoin price trends using deep learning models, including LSTMs and Transformers. By leveraging historical price data and market indicators, the model aims to capture temporal dependencies and improve forecasting accuracy.

A deep learning-based LSTM model to forecast future stock prices using historical trends, with visualizations and modular code for financial time-series analysis.

Analyzes daily petrol and diesel prices in Kerala (2024–2025) using SQL, Python, Power BI, and Streamlit. It uncovers trends, detects anomalies, clusters districts, and forecasts prices with Machine Learning and Deep Learning models.

📊 Stock Price Forecasting with LSTM — A deep learning project that predicts future stock trends using daily-updating world stock market data (from Kaggle, Marketstack, StockData.org, and other global sources). Built and deployed on Google Colab with visualization, evaluation, and reproducible code.

An interactive full-stack stock forecasting dashboard that uses LSTM deep learning models to predict future stock prices of major companies across China, Japan, and India. The project combines a Flask backend, TensorFlow-based time-series modeling, and a React-powered interactive dashboard to visualize current and forecasted price trends with high

A deep learning–based fusion framework that integrates multi-source technical indicators with sequence modeling to predict stock trends across multiple tickers. It uses advanced feature engineering, sliding-window time-series processing, and LSTM architectures to generate reliable UP/DOWN/NEUTRAL trend and price forecasts.

Developed a deep learning model using Long Short-Term Memory (LSTM) networks to predict Bitcoin’s closing price based on historical market data. The project aimed to capture the temporal dependencies and volatility of Bitcoin prices to forecast future trends with higher accuracy than traditional machine learning models.

A project that analyzes historical Bitcoin prices to forecast future trends with deep learning and statistical models (LSTM, BiLSTM, ARIMA and Transformer). The Transformer-based approach achieves the highest accuracy, leveraging time-series data for improved financial decision-making.

This project applies Deep Learning with LSTM (Long Short-Term Memory) networks to predict stock prices based on historical data. By learning sequential patterns in time-series data, the model forecasts future trends, helping investors and analysts make informed decisions in stock market prediction.

StoxAI is an AI-powered stock market prediction platform that combines deep learning (LSTM) with real-time news sentiment analysis to forecast price trends. It analyzes historical market data and financial news to deliver smarter insights, multi-timeframe predictions, and data-driven investment support.

This project is a deep learning-powered web application that predicts stock market prices using Long Short-Term Memory (LSTM) networks. Built with Python and Streamlit, the app fetches historical stock data using the yFinance API and forecasts future stock prices based on time-series trends. The interface provides intuitive charts, moving average a

Deep learning case study using LSTM to predict stock price direction and forecast trends with Tiingo data.

Interactive Agriculture Price Analysis Dashboard with Deep Learning Forecasting (LSTM/GRU). Analyze historical crop prices across Indian markets and predict future trends using advanced time series models.

Predicts Walgreens (WBA) stock prices using LSTM deep learning. Implements multivariate time series forecasting with historical stock data to analyze trends and make future price predictions. Built with Python, TensorFlow, and Keras.

This project analyzes and predicts stock market trends using time series forecasting techniques. It leverages historical stock price data with machine learning and deep learning models to identify patterns, visualize trends, and make accurate future predictions.

Time series analysis and prediction of Amazon stock prices using LSTM. Built with PyTorch, this project leverages deep learning for forecasting trends, featuring data preprocessing, model training, and evaluation.

Streamlit app for stock price prediction using Keras, yFinance, and Matplotlib. Fetches live data, visualizes trends, and predicts prices with a neural network model. Highlights preprocessing, interactive visuals, and deep learning for time series forecasting in finance.

Stock Price Forecasting with Time Series & Deep Learning A project to forecast stock prices using ARIMA, SARIMA, Prophet, and LSTM models. It compares classical and deep learning methods on historical stock data to predict future trends. The LSTM model achieved low training loss (~1.3e-4). An interactive Streamlit dashboard is included

Microsoft Stock Price Prediction App — An interactive Streamlit app using LSTM deep learning to forecast Microsoft stock prices with 30-day future predictions. or slightly shorter: 📊 Predict Microsoft stock prices with an LSTM-based Streamlit app — visualize trends and forecast the next 30 days.

Forecast Netflix daily closing prices using ARIMA, LightGBM, and LSTM with the Darts library. Analyze trends, build predictive models, and compare performance using MAPE and RMSE, demonstrating how statistical, ML, and deep learning approaches perform in financial time series forecasting

LSTM-based stock price forecasting system with technical indicators (RSI, MACD, Bollinger Bands). Provides 30-day price predictions with trend analysis and risk metrics. Features backtesting, Sharpe ratio calculations, volatility analysis, and trading signal generation. Demonstrates deep learning for time-series prediction.

LSTM-based model predicting NIFTY index prices using historical time-series data. Captures temporal patterns, learns trends, and evaluates prediction accuracy. Focused on exploring deep learning for financial forecasting with sequential market data.

Stock Price Prediction uses machine learning and deep learning to forecast stock market trends from historical data. It applies time-series analysis, regression, and LSTM models for accurate predictions, with visualizations to interpret results. The project helps researchers and investors explore AI-driven financial forecasting.

Stock Price Prediction Model A deep learning-based project for forecasting stock prices using historical data. The model leverages time series analysis and regression techniques to predict future stock trends, evaluated with visual comparisons of predicted vs. actual prices. Built with Python (Pandas, Scikit-learn, Keras)

Finsight is a deep learning project that predicts stock prices using LSTM networks. It preprocesses historical market data, trains models to capture time-series patterns, and visualizes actual vs. predicted trends. Ideal for learning financial forecasting with AI.

Crypto-Candle-Stick-Prediction is a project focused on analyzing and forecasting cryptocurrency price movements using candlestick chart patterns. By leveraging machine learning and deep learning techniques, the project aims to identify trends and potential market shifts based on historical price data. It combines technical analysis with predictive