Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts
How can we efficiently correlate multiple stocks for accurate stock movement prediction?
Stock movement prediction has received growing interest in data mining and machine …
Stock movement prediction has received growing interest in data mining and machine …
Stock market prediction using deep learning algorithms
Abstract The Stock Market is one of the most active research areas, and predicting its nature
is an epic necessity nowadays. Predicting the Stock Market is quite challenging, and it …
is an epic necessity nowadays. Predicting the Stock Market is quite challenging, and it …
HGNN: Hierarchical graph neural network for predicting the classification of price-limit-hitting stocks
In some stock markets, stock prices are not allowed to rise above a daily limit to restrain the
surge of price (called price limit). When the price limit occurs, investors tend to chase the …
surge of price (called price limit). When the price limit occurs, investors tend to chase the …
Reinforcement learning for quantitative trading
Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …
techniques in analyzing the financial market, has been a popular topic in both academia and …
Stock trend prediction with multi-granularity data: A contrastive learning approach with adaptive fusion
Stock trend prediction plays a crucial role in quantitative investing. Given the prediction task
on a certain granularity (eg, daily trend), a large portion of existing studies merely leverage …
on a certain granularity (eg, daily trend), a large portion of existing studies merely leverage …
MDF-DMC: A stock prediction model combining multi-view stock data features with dynamic market correlation information
Z Yang, T Zhao, S Wang, X Li - Expert Systems with Applications, 2024 - Elsevier
Using machine learning coupled with stock price data to predict stock price trends has
attracted increasing attention from data mining and machine learning communities. An …
attracted increasing attention from data mining and machine learning communities. An …
Natural visibility encoding for time series and its application in stock trend prediction
As a newly developed method, the natural visibility graph (NVG) has attracted great
attention. Most of the previous research focuses on exploring the time series using the NVG …
attention. Most of the previous research focuses on exploring the time series using the NVG …
Multi-granularity residual learning with confidence estimation for time series prediction
Time-series prediction is of high practical value in a wide range of applications such as
econometrics and meteorology, where the data are commonly formed by temporal patterns …
econometrics and meteorology, where the data are commonly formed by temporal patterns …
Dynamic graph construction via motif detection for stock prediction
Stock trend prediction is crucial for recommending high-investment value stocks and can
strongly assist investors in making decisions. In recent years, the significance of stock …
strongly assist investors in making decisions. In recent years, the significance of stock …
Multivariate time series forecasting using multiscale recurrent networks with scale attention and cross-scale guidance
Multivariate time series (MTS) forecasting is considered as a challenging task due to
complex and nonlinear interdependencies between time steps and series. With the advance …
complex and nonlinear interdependencies between time steps and series. With the advance …