Predicting Stock Prices Using LSTM Networks: A Comprehensive Approach

Meghana R
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Abstract

Abstract: Prediction of stock prices calls for strong algorithmic foundations for predictions of greater magnitude in share prices because the stock market epitomizes volatility. There exist several models used for the prediction of stock prices. The Long ShortTerm Memory algorithm is one model that seems well-suited for such time series problems. The key objective is to best predict current trends in the market and stock prices, which can be done through point prediction, scenario prediction, anomaly prediction, interval prediction, and volatility prediction. The objective of the study is to provide insight to investors and analysts to understand and predict the behavior of the stock market
使用 LSTM 网络预测股票价格:综合方法
摘要:由于股票市场是波动性的缩影,因此股票价格预测需要强大的算法基础,以预测更大的股票价格。目前有几种用于预测股票价格的模型。长短期记忆算法是一种似乎非常适合此类时间序列问题的模型。主要目标是通过点预测、情景预测、异常预测、区间预测和波动预测等方法,对市场和股票价格的当前趋势进行最佳预测。本研究的目的是为投资者和分析师提供洞察力,以了解和预测股票市场的行为
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