Forecasting stock prices using neural networks, Abs...
Forecasting stock prices using neural networks, Abstract: This paper investigates the application of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D CNN), and logistic regression (LR), for predicting stock trends The work in this paper aims at integrating sentiment analysis and historical stock data to improve the accuracy of stock price forecasting. 2013 2 2 49-55 [9] Donaldson RG and Kamstra M Forecast combining with neural networks The empirical results show that the deep learning model has significant advantages in capturing the nonlinear characteristics of stock prices, and is better than the traditional time series method in all The stock price prediction of listed companies and the analysis of influencing factors are the core topics of this paper. Outlines the commonly used datasets and various evaluation metrics in the field of stock forecasting. elet transform to process stock data and LSTM neural network based o attention to forecasting the opening price of stocks and achieved good results. Successful prediction of a stock's future price can yield Introduction: In the ever-evolving landscape of finance, predicting stock prices remains a challenging yet essential task for investors, traders, and This project predicts future stock closing prices using Deep Learning time-series models and compares the performance of multiple neural network architectures: Vanilla RNN LSTM (Long Short-Term M Deep learning architectures, particularly Recurrent Neural Networks (RNNs) and Attention-Based models, have become pivotal in financial time-series forecasting due to their superior ability to This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. It provides real-time predictions, AI trading suggestions (Buy/Hold/S Dive into the world of Long Short-Term Memory networks and discover how they overcome the vanishing gradient problem in recurrent neural networks. In addition, some researchers have also The objective of this project is to study and implement a memory-based neural network (LSTM) for time-series forecasting. J. Let's see how each layer in a CNN This TensorFlow implementation of an LSTM neural network can be used for time series forecasting. First, this paper shows This project predicts future stock closing prices using Deep Learning time-series models and compares the performance of multiple neural network architectures: Vanilla RNN LSTM (Long Short-Term M AI Stock Price Predictor is a Flask-based web application that uses an LSTM Neural Network to forecast short-term stock prices. Neural networks attempt to predict future price Abstract Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best Novel RNN-based hybrid models are proposed to forecast one-time-step and multi-time-step closing prices of the DAX, DOW, and S&P500 indices Convolutional Neural Networks can be effectively applied to time-series data such as stock price prediction. Chalvatzis, D. As of the end of June 2022, the number of A-share investors in my country reached Results demonstrate that the proposed BiLSTM-Attention model significantly outperforms baseline models across all evaluation metrics, validating the effectiveness of combining bidirectional [3] Mishra A K, Renganathan J, Gupta A. Hristu-Varsakelis, High-performance stock index trading via neural networks and trees, Appl. LSTM Recurrent Neural Networks (RNNs) are designed to handle sequential data by maintaining a hidden state that captures information from previous time steps. C. Applied Sciences, 15 (8 Analytics Insight is publication focused on disruptive technologies such as Artificial Intelligence, Big Data Analytics, Blockchain and Cryptocurrencies. 96 (2020). Zahedi, Javad, Rounaghi, Mohammad Mahdi (2015) Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. By the end of this course, you'll be able to: - An end-to-end Machine Learning project that predicts the next 7 days stock prices using LSTM (Long Short-Term Memory) neural networks and provides a simple interactive UI built with Streamlit. Soft Comput. This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting As a case study, thirty-three companies’ representative of the S&P 500 are selected, and a multilayer perceptron artificial neural network is built and trained with input parameter indicators of This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture. Reviews the literature on data-driven neural networks in the field of stock forecasting. The CGAN model learns the data generation distribution and This paper proposes a Generative Adversarial Network (GAN) framework with the Convolution Neural Networks (CNN) as the discriminator and a hybrid model as the generator for forecasting the stock A novel approach for predicting the volatility of multiple indices in the U. In our study, we have used Sentiment scores, which were Feng, Ruizhe, Jiang, Shanshan, Liang, Xingyu, Xia, Min (2025) STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction. The aim of this study is to propose Recurrent Neural Network (RNN) model that [8] Devadoss AV and Ligori TAA Forecasting of stock prices using multi layer perceptron Int. S. The SHOAGAI‐TSF technique uses probabilistic forecasting with a conditional GAN (CGAN) approach for the prediction of stock prices. However they often face challenges in This study presents an AI-Powered Real-Time Stock Price Estimator that leverages machine learning techniques to forecast stock prices based on live market data. stock market by integrating historical closing prices with sentiment analysis of Twitter data using deep neural networks, which The goal of this paper is to build a trading algorithm by applying image recognition neural network - Convolutional Neural Network(CNN) - to the 2D technical candle stick charts. Web Technol. . Given historical stock price data, the model learns temporal dependencies and The use of traditional statistics methods in forecasting time series are less practicality and gives less valuable prediction.