[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission
Predictive analytics utilizing machine learning algorithms play a pivotal role in various
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
G Lin, A Lin, D Gu - Information Sciences, 2022 - Elsevier
The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …
passengers as well as enhancing the efficiency of traffic management in the urban …
A machine learning approach to predict air quality in California
Predicting air quality is a complex task due to the dynamic nature, volatility, and high
variability in time and space of pollutants and particulates. At the same time, being able to …
variability in time and space of pollutants and particulates. At the same time, being able to …
A survey on machine learning models for financial time series forecasting
Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
Predicting the direction of stock market prices using tree-based classifiers
Predicting returns in the stock market is usually posed as a forecasting problem where
prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of …
prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of …
Price movement prediction of cryptocurrencies using sentiment analysis and machine learning
F Valencia, A Gómez-Espinosa, B Valdés-Aguirre - entropy, 2019 - mdpi.com
Cryptocurrencies are becoming increasingly relevant in the financial world and can be
considered as an emerging market. The low barrier of entry and high data availability of the …
considered as an emerging market. The low barrier of entry and high data availability of the …
Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model
X Wei, C Rao, X Xiao, L Chen, M Goh - Expert systems with applications, 2023 - Elsevier
Cardiovascular disease (CVD) has become a significant public health problem affecting
national economic and social development, and ranks among the top causes of death in the …
national economic and social development, and ranks among the top causes of death in the …
Forecasting daily stock market return using dimensionality reduction
In financial markets, it is both important and challenging to forecast the daily direction of the
stock market return. Among the few studies that focus on predicting daily stock market …
stock market return. Among the few studies that focus on predicting daily stock market …
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
Big data analytic techniques associated with machine learning algorithms are playing an
increasingly important role in various application fields, including stock market investment …
increasingly important role in various application fields, including stock market investment …