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We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method.
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Oct 22, 2024 · We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method.
Abstract: Forecasting performances of feed-forward and recurrent neural networks (NN) trained with different learning algorithms are analyzed and compared ...
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In this chapter, we will describe the basics of traditional time series analyses, discuss how neural net- works work, show how to implement time series ...
In developing a feedforward neural network model for forecasting tasks, specifying its architecture in terms of the number of input, hidden, and output neurons ...
This paper contains a financial forecast using Artificial Neural Networks. The analysis uses the traditional Backpropagation algorithm, followed by Resilient ...
In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/ ...
FORECASTING: ACCURACY AND ROBUSTNESS ANALISYS​​ This paper aims to analyze the neural networks for financial time series forecasting. Specifically the ability to ...
This paper will develop, implement a multilayer feedforward neural network based financial time series forecasting system that will be used to predict the ...
The objective of this paper is to provide a practical introductory guide in the design of a neural network for forecasting economic time series data.