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 …

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 …

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 …

Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction

S Asadi, E Hadavandi, F Mehmanpazir… - Knowledge-Based …, 2012 - Elsevier
Artificial Intelligence models (AI) which computerize human reasoning has found a
challenging test bed for various paradigms in many areas including financial time series …

Soft-computing techniques and ARMA model for time series prediction

I Rojas, O Valenzuela, F Rojas, A Guillén, LJ Herrera… - Neurocomputing, 2008 - Elsevier
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 …

An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran

A Azadeh, M Saberi, O Seraj - Energy, 2010 - Elsevier
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 …

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 …

Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis

A Kheirkhah, A Azadeh, M Saberi, A Azaron… - Computers & Industrial …, 2013 - Elsevier
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 …

Improved estimation of electricity demand function by integration of fuzzy system and data mining approach

A Azadeh, M Saberi, SF Ghaderi, A Gitiforouz… - Energy Conversion and …, 2008 - Elsevier
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 …

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 …