Computational intelligence and financial markets: A survey and future directions
RC Cavalcante, RC Brasileiro, VLF Souza… - Expert Systems with …, 2016 - Elsevier
Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …
modern society. In these kinds of markets, information is an invaluable asset. However, with …
Stock price prediction based on deep neural networks
P Yu, X Yan - Neural Computing and Applications, 2020 - Springer
Understanding the pattern of financial activities and predicting their development and
changes are research hotspots in academic and financial circles. Because financial data …
changes are research hotspots in academic and financial circles. Because financial data …
Time series outlier detection based on sliding window prediction
Y Yu, Y Zhu, S Li, D Wan - Mathematical problems in …, 2014 - Wiley Online Library
In order to detect outliers in hydrological time series data for improving data quality and
decision‐making quality related to design, operation, and management of water resources …
decision‐making quality related to design, operation, and management of water resources …
Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction
Artificial Intelligence models (AI) which computerize human reasoning has found a
challenging test bed for various paradigms in many areas including financial time series …
challenging test bed for various paradigms in many areas including financial time series …
Soft-computing techniques and ARMA model for time series prediction
The challenge of predicting future values of a time series covers a variety of disciplines. The
fundamental problem of selecting the order and identifying the time varying parameters of an …
fundamental problem of selecting the order and identifying the time varying parameters of an …
An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran
This study presents an integrated fuzzy regression and time series framework to estimate
and predict electricity demand for seasonal and monthly changes in electricity consumption …
and predict electricity demand for seasonal and monthly changes in electricity consumption …
Novelty detection in time series using self-organizing neural networks: A comprehensive evaluation
L Aguayo, GA Barreto - Neural Processing Letters, 2018 - Springer
In this survey paper, we report the results of a comprehensive study involving the application
of dynamic self-organizing neural networks (SONNs) to the problem of novelty detection in …
of dynamic self-organizing neural networks (SONNs) to the problem of novelty detection in …
Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis
Due to various seasonal and monthly changes in electricity consumption and difficulties in
modeling it with the conventional methods, a novel algorithm is proposed in this paper. This …
modeling it with the conventional methods, a novel algorithm is proposed in this paper. This …
Improved estimation of electricity demand function by integration of fuzzy system and data mining approach
This study presents an integrated fuzzy system, data mining and time series framework to
estimate and predict electricity demand for seasonal and monthly changes in electricity …
estimate and predict electricity demand for seasonal and monthly changes in electricity …
Software effort estimation using machine learning techniques with robust confidence intervals
PL Braga, ALI Oliveira… - … international conference on …, 2007 - ieeexplore.ieee.org
The precision and reliability of the estimation of the effort of software projects is very
important for the competitiveness of software companies. Good estimates play a very …
important for the competitiveness of software companies. Good estimates play a very …