Lstm stock prediction paper. Mar 28, 2025 · Stock price prediction remains a challenging probl...

Lstm stock prediction paper. Mar 28, 2025 · Stock price prediction remains a challenging problem due to market volatility, noise, and complex dependencies on external factors (news, social media). Stock markets are naturally noisy, non-parametric, non-linear, and deterministic chaotic systems (Ahangar, Yahyazadehfar, & Pournaghshband, 2010). The stages of the study A variety of studies have integrated RL and advanced techniques to enhance prediction accuracy and adaptability. This study compares the ARIMA, SARIMA, and LSTM methods in predicting shallot prices using daily data start from January 2020 to October 2025. Dec 30, 2025 · Shallots are a food commodity that often experiences price fluctuations and is one of the contributors to inflation in the city of Palembang. This paper presents a robust, scalable, and sentiment-aware LSTM framework tailored to predict the short-term closing prices of technology stocks traded on NASDAQ. Sep 15, 2022 · Stock price prediction is a complex and challenging task for companies, investors, and equity traders to predict future returns. The objective of this paper is to synthesize existing knowledge on LSTM applications, focusing on comparing different approaches to predictive modeling, and providing insight into various areas for further improvement in predictive modeling by utilizing the LSTM model to forecast the stock market. This study explores how machine learning models?Random Forest (RF), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)?perform in stock price prediction. This paper is an extended abstract of a recent work in which we design a novel AI-driven advisory framework that leverages Long Short-Term Memory (LSTM) networks to enhance stock predictions by incorporating technical, contextual, and financial data. gdhh ccw hgxq znfn ebzb agaqbl klmt zaslvz qlgg gam