Stock price prediction using CNN-BiLSTM-Attention model
J Zhang, L Ye, Y Lai - Mathematics, 2023 - mdpi.com
Accurate stock price prediction has an important role in stock investment. Because stock
price data are characterized by high frequency, nonlinearity, and long memory, predicting …
price data are characterized by high frequency, nonlinearity, and long memory, predicting …
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches
CF Tsai, YC Hsiao - Decision support systems, 2010 - Elsevier
To effectively predict stock price for investors is a very important research problem. In
literature, data mining techniques have been applied to stock (market) prediction. Feature …
literature, data mining techniques have been applied to stock (market) prediction. Feature …
Modeling uncertainty with fuzzy logic
A Celikyilmaz, IB Turksen - Studies in fuzziness and soft computing, 2009 - Springer
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this
afternoon? Should I take an umbrella with me? Will I be able to find parking near the …
afternoon? Should I take an umbrella with me? Will I be able to find parking near the …
A support vector clustering method
We present a novel kernel method for data clustering using a description of the data by
support vectors. The kernel reflects a projection of the data points from data space to a high …
support vectors. The kernel reflects a projection of the data points from data space to a high …
Enhanced fuzzy system models with improved fuzzy clustering algorithm
A Celikyilmaz, IB Turksen - IEEE Transactions on Fuzzy …, 2008 - ieeexplore.ieee.org
Although traditional fuzzy models have proven to have high capacity of approximating the
real-world systems, they have some challenges, such as computational complexity …
real-world systems, they have some challenges, such as computational complexity …
Predicting stock market price using support vector regression
In this study, support vector regression (SVR) analysis is used as a machine learning
technique in order to predict the stock market price as well as to predict stock market trend …
technique in order to predict the stock market price as well as to predict stock market trend …
Financial latent Dirichlet allocation (FinLDA): Feature extraction in text and data mining for financial time series prediction
N Kanungsukkasem, T Leelanupab - IEEE Access, 2019 - ieeexplore.ieee.org
News has been an important source for many financial time series predictions based on
fundamental analysis. However, digesting a massive amount of news and data published on …
fundamental analysis. However, digesting a massive amount of news and data published on …
Artificial intelligence applications in financial forecasting–a survey and some empirical results
BB Nair, VP Mohandas - Intelligent Decision Technologies, 2015 - content.iospress.com
Financial forecasting is an area of research which has been attracting a lot of attention
recently from practitioners in the field of artificial intelligence. Apart from the economic …
recently from practitioners in the field of artificial intelligence. Apart from the economic …
An efficient modelling approach for forecasting financial time series data using support vector regression and windowing operators
Forecasting or predicting stock market price and trend is regarded as a challenging task
because of its chaotic nature. Stock market is essentially a nonlinear, non-parametric, noisy …
because of its chaotic nature. Stock market is essentially a nonlinear, non-parametric, noisy …
Uncertainty modeling of improved fuzzy functions with evolutionary systems
A Celikyilmaz, IB Turksen - IEEE Transactions on Systems …, 2008 - ieeexplore.ieee.org
This paper introduce a type-2 fuzzy function system for uncertainty modeling using
evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy …
evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy …