scholarly journals Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks

2011 ◽  
Vol 60 (7) ◽  
pp. 070512
Author(s):  
Li He ◽  
Yang Zhou ◽  
Zhang Yi-Min ◽  
Wen Bang-Chun
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Prediction Performance ◽  
Embedding Dimension ◽  
Radial Basis

scholarly journals Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Zuzana Rowland ◽  
George Lazaroiu ◽  
Ivana Podhorská
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Radial Basis Function ◽  
Basis Function ◽  
Academic Research ◽  
Multi Layer Perceptron ◽  
Seasonal Fluctuations ◽  
Radial Basis ◽  
Chinese Yuan

The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks.

Forecasting High Frequency Data: An ARMA-Soft RBF Network Model for Time Series

2014 ◽  
Vol 596 ◽  
pp. 160-163
Author(s):  
Dusan Marcek
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Series Data ◽  
Rbf Network ◽  
Forecasting Models ◽  
Radial Basis

In the article we alternatively develop forecasting models based on the Box-Jenkins methodology and on the neural approach based on classic and fuzzy logic radial basis function neural networks. We evaluate statistical and neuronal forecasting models for monthly platinum price time series data. In the direct comparison between statistical and neural models, the experiment shows that the neural approach clearly improve the forecast accuracy. Following fruitful applications of neural networks to predict financial data this work goes on. Both approaches are merged into one output to predict the final forecast values. The proposed novel approach deals with nonlinear estimate of various radial basis function neural networks.

Radial basis function neural networks and temporal fusion for the classification of bioacoustic time series

2003 ◽  
Vol 51 ◽  
pp. 265-275 ◽  
Author(s):  
Friedhelm Schwenker ◽  
Christian Dietrich ◽  
Hans A. Kestler ◽  
Klaus Riede ◽  
Günther Palm
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis
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