Specifically, we have focused on the stock prediction problem for the Vietnamese market in the short and long term. Long short-term memory (LSTM) based on deep learning model has been applied to big data problem such as VN-INDEX. We compared the prediction results of the variants of the ...
Technology data in general and company specific data of Microsoft, Google and IBM are used to test the effect of the headlines on the stock market. Two different approaches are experimented with: sentiment analysis and LSTM. The results show that it is possible to predict the stock market with...
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks...
Lgfc-cnn: prediction of lncrna-protein interactions by using multiple types of features through deep learning. Genes. 2021;12(11):1689. Article PubMed PubMed Central CAS Google Scholar Song D, Baek AMC, Kim N. Forecasting stock market indices using padding-based fourier transform denoising ...
Air quality index prediction using an effective hybrid deep learning model. Environ Pollut. 2022;315: 120404. Article Google Scholar Gilik A, Ogrenci AS, Ozmen A. Air quality prediction using CNN+ LSTM-based hybrid deep learning architecture. Environ Sci Pollut Res. 2022;29:1–19. Article ...
Review of Stock Price Predicting Method Based on LSTM Stock market forecasting is a challenging field for investors to make profits in the financial market. Investors need to understand that financial markets ... H Qian - 《Advances in Economics Management & Political Sciences》 被引量: 0发表: ...
In this work, we propose and validate a multi-level stacking model of long short-term memory (LSTM) units for the short-term prediction of stock-index closing prices. The proposed machine-learning model is trained using historical data to predict next-day closing prices. The first layer of ...
Aiming to better predict daily stock prices and fluctuations caused by high noise non-stationary data in actual trading, we propose a hybrid deep learning framework based on Singular Spectrum Analysis (SSA), multiple feature selection, and Long Short TermMemory (LSTM) network optimized by Particle ...
Using deep unsupervised learning (Self-organized Maps) we will try to spot anomalies in every day's pricing. Anomaly (such as a drastic change in pricing) might indicate an event that might be useful for the LSTM to learn the overall stock pattern. Next, having so many features, we need...
Using deep unsupervised learning (Self-organized Maps) we will try to spot anomalies in every day's pricing. Anomaly (such as a drastic change in pricing) might indicate an event that might be useful for the LSTM to learn the overall stock pattern. Next, having so many features, we need...