Journal ArticleDOI
Artificial neural networks for non-stationary time series
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TLDR
This paper investigates whether it is feasible to relax the stationarity condition to non-stationary time series and finds that overfitting by ANN could be useful in the analysis of such non- stationary complex financial time series.About:
This article is published in Neurocomputing.The article was published on 2004-10-01. It has received 91 citations till now. The article focuses on the topics: Overfitting & STAR model.read more
Citations
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Artificial neural networks
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Journal ArticleDOI
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
TL;DR: A novel deep learning framework where wavelet transforms, stacked autoencoders and long-short term memory are combined for stock price forecasting and shows that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Journal ArticleDOI
On the use of cross-validation for time series predictor evaluation
TL;DR: It is suggested that the use of a blocked form of cross-validation for time series evaluation became the standard procedure, thus using all available information and circumventing the theoretical problems.
Journal ArticleDOI
Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
TL;DR: An original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP) and the multi-layer perceptron (MLP) is proposed.
Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TL;DR: The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.
References
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Journal ArticleDOI
Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
Journal ArticleDOI
Time series forecasting using a hybrid ARIMA and neural network model
TL;DR: Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
Book
Artificial Neural Networks
TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
Book
The Statistical Analysis of Time Series
TL;DR: The Wiley Classics Library as discussed by the authors is a collection of books that have become recognized classics in their respective fields, including some of the most important works of the 20th century in mathematics.
Artificial neural networks
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.