Journal ArticleDOI
A dynamic architecture for artificial neural networks
M. Ghiassi,H. Saidane +1 more
TLDR
This paper introduces a model that uses a different architecture compared to the traditional neural network, to capture and forecast nonlinear processes, and shows that this approach performs well when compared with traditional models and established research.About:
This article is published in Neurocomputing.The article was published on 2005-01-01. It has received 107 citations till now. The article focuses on the topics: Time delay neural network & Neural gas.read more
Citations
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Journal ArticleDOI
An artificial neural network (p,d,q) model for timeseries forecasting
Mehdi Khashei,Mehdi Bijari +1 more
TL;DR: The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks, and can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Journal ArticleDOI
Using artificial neural network models in stock market index prediction
TL;DR: The effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions are evaluated, including multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables.
Journal ArticleDOI
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
Mehdi Khashei,Mehdi Bijari +1 more
TL;DR: Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid models and also either of the components models used separately.
Journal ArticleDOI
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network
TL;DR: This research introduces an approach to supervised feature reduction using n-grams and statistical analysis to develop a Twitter-specific lexicon for sentiment analysis, and develops sentiment classification models using this reduced lexicon and the DAN2 machine learning approach, which has demonstrated success in other text classification problems.
Journal ArticleDOI
A dynamic artificial neural network model for forecasting time series events
TL;DR: A dynamic neural network model for forecasting time series events that uses a different architecture than traditional models is presented and shows that this approach is more accurate and performs significantly better than the traditional neural network and autoregressive integrated moving average (ARIMA) models.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
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Self-organized formation of topologically correct feature maps
TL;DR: In this paper, the authors describe a self-organizing system in which the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events.
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Approximation capabilities of multilayer feedforward networks
TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.
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Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.