Year: 2025
Type: article
Abstract: This project can help investors make well-informed decisions, stock market prediction has been a focus of financial study. However, reliable forecasting is a difficult task because stock prices show highly volatile and nonlinear characteristics. The complex dependencies in stock market data are frequently missed by conventional statistical methods like linear regression and autoregressive integrated moving average (ARIMA). Deep learning methods, especially Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs), have been popular in recent years as efficient time-series forecasting tools.
Authors: Jai Singh -, Saloni Sonkusare -, Aniket Binewale -, Shivam Kunwar -, Shraddha Patle -, Dr. Archana Potnurwar -
Institution: Priyadarshini College of Engineering
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409696156