A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting

Accurate forecasts of stock indexes can not only provide reference information for investors to formulate relevant strategies but also provide effective channels for the government to regulate the market. However, due to its volatility and complexity, predicting the stock price index has always been...

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Vydáno v:	Mathematics (Basel) Ročník 12; číslo 23; s. 3778
Hlavní autoři:	He, Xuecheng, Wang, Jujie
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.12.2024
Témata:	Accuracy Algorithms Analysis Data compression Efficiency Forecasting Forecasts and trends Gaussian process interval prediction Investments Machine learning Multivariate analysis Optimization Political aspects Prices and rates recursive feature elimination with cross-validation Root-mean-square errors stack autoencoder Statistical analysis Stock exchanges stock index forecasting Stock price indexes Stock prices Stocks Support vector machines Trends Variables Volatility
ISSN:	2227-7390, 2227-7390
On-line přístup:	Získat plný text
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