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 …
Sailing through the COVID‐19 Crisis by Using AI for Financial Market Predictions
The outbreak of COVID‐19 has brought the world to an unprecedented position where
financial and mental resources are drying up. Livelihoods are being lost, and it is becoming …
financial and mental resources are drying up. Livelihoods are being lost, and it is becoming …
[图书][B] Advanced data mining techniques
The intent of this book is to describe some recent data mining tools that have proven
effective in dealing with data sets which often involve unc-tain description or other …
effective in dealing with data sets which often involve unc-tain description or other …
Predicting stock price changes based on the limit order book: a survey
This survey starts with a general overview of the strategies for stock price change predictions
based on market data and in particular Limit Order Book (LOB) data. The main discussion is …
based on market data and in particular Limit Order Book (LOB) data. The main discussion is …
Machine learning techniques for stock price prediction and graphic signal recognition
Stock market analysis is extremely important for investors because knowing the future trend
and grasping the changing characteristics of stock prices will decrease the risk of investing …
and grasping the changing characteristics of stock prices will decrease the risk of investing …
Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images
This paper aims to forecast stock price Turning Points (TPs) with a developed hybrid
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. To this …
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. To this …
Predicting the trend of dissolved oxygen based on the kPCA-RNN model
Water quality forecasting is increasingly significant for agricultural management and
environmental protection. Enormous amounts of water quality data are collected by …
environmental protection. Enormous amounts of water quality data are collected by …
A hybrid artificial neural network with metaheuristic algorithms for predicting stock price
R Ghasemiyeh, R Moghdani, SS Sana - Cybernetics and systems, 2017 - Taylor & Francis
Most investors change stock prices in long-term businesses because of global turbulence in
the markets. Consequently, prediction of stock price is a difficult task because of unknown …
the markets. Consequently, prediction of stock price is a difficult task because of unknown …
Extending classical surrogate modeling to high dimensions through supervised dimensionality reduction: a data-driven approach
Thanks to their versatility, ease of deployment, and high performance, surrogate models
have become staple tools in the arsenal of uncertainty quantification (UQ). From local …
have become staple tools in the arsenal of uncertainty quantification (UQ). From local …
Pattern graph tracking-based stock price prediction using big data
Stock price forecasting is the most difficult field owing to irregularities. However, because
stock prices sometimes show similar patterns and are determined by a variety of factors, we …
stock prices sometimes show similar patterns and are determined by a variety of factors, we …