scholarly journals Evolutionary product unit based neural networks for hydrological time series analysis

2010 ◽  
Vol 13 (4) ◽  
pp. 825-841 ◽  
Author(s):  
Dulakshi S. K. Karunasingha ◽  
A. W. Jayawardena ◽  
W. K. Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Meteorological Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Engineering Analysis ◽  
Neural Network Models ◽  
Mlp Neural Network ◽  
Evolutionary Product

Artificial Neural Networks (ANNs) are now widely used in many areas of science, medicine, finance and engineering. Analysis and prediction of time series of hydrological/and meteorological data is one such application. Problems that still exist in the application of ANN's are the lack of transparency and the expertise needed for training. An evolutionary algorithm-based method to train a type of neural networks called Product Units Based Neural Networks (PUNN) has been proposed in a 2006 study. This study investigates the applicability of this type of neural networks to hydrological time series prediction. The technique, with a few small changes to improve the performance, is applied to some benchmark time series as well as to a real hydrological time series for prediction. The results show that evolutionary PUNN produce more transparent models compared to widely used multilayer perceptron (MLP) neural network models. It is also seen that training of PUNN models requires less expertise compared to MLPs.

scholarly journals On the prediction of solar activity using different neural network models

1996 ◽  
Vol 14 (1) ◽  
pp. 20-26 ◽  
Author(s):  
F. Fessant ◽  
S. Bengio ◽  
D. Collobert
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Solar Activity ◽  
Time Series Prediction ◽  
Network Models ◽  
Accurate Prediction ◽  
Experimental Results ◽  
Neural Network Models ◽  
Telecommunication Companies

Abstract. Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters rely strongly on solar activity. In this paper, we analyze the use of neural networks for sunspot time series prediction. Three types of models are tested and experimental results are reported for a particular sunspot time series: the IR5 index.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

scholarly journals Development of models for maximum and time variation of storm surges at the Tanshui estuary

2014 ◽  
Vol 14 (9) ◽  
pp. 2313-2320 ◽  
Author(s):  
C.-P. Tsai ◽  
C.-Y. You
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Variation ◽  
Network Models ◽  
Storm Surges ◽  
Physical Parameters ◽  
Neural Network Models ◽  
Modeling And Forecasting ◽  
Time Variations

Abstract. In this study, artificial neural networks, including both multilayer perception and the radial basis function neural networks, are applied for modeling and forecasting the maximum and time variation of storm surges at the Tanshui estuary in Taiwan. The physical parameters, including both the local atmospheric pressure and the wind field factors, for finding the maximum storm surges, are first investigated based on the training of neural networks. Then neural network models for forecasting the time series of storm surges are accordingly developed using the major meteorological parameters with time variations. The time series of storm surges for six typhoons were used for training and testing the models, and data for three typhoons were used for model forecasting. The results show that both neural network models perform very well for the forecasting of the time variation of storm surges.

scholarly journals Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

2018 ◽  
Vol 218 ◽  
pp. 03021 ◽  
Author(s):  
Bagus Priambodo ◽  
Yuwan Jumaryadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Historical Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Traffic Data ◽  
Data Traffic ◽  
Neural Network Models

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

2010 ◽  
Vol 39 ◽  
pp. 375-382 ◽  
Author(s):  
Zhao Cheng Liu ◽  
Xi Yu Liu ◽  
Zi Ran Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rates ◽  
Transfer Functions ◽  
Rbf Neural Network ◽  
Network Models ◽  
Garch Models ◽  
Rbf Neural Networks ◽  
Neural Network Models

The CNY exchange rates can be viewed as financial time series which are characterized by high uncertainty, nonlinearity and time-varying behavior. Predictions for CNY exchange rates of GBP-CNY and USD-CNY were carried out respectively by means of RBF neural network forecasters and GARCH models. GARCH is a mechanism that includes past variances in the explanation of future variances and a time-series technique that we use to model the serial dependence of volatility. The detailed design of architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. While experimental results show that the performance of RBF neural networks for forecasting spot CNY exchange rates is better than that of GARCH, both of them are acceptable and effective especially in short term predictions.

NON-STATIONARY TIME SERIES PREDICTION USING ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK MODELS

2020 ◽  
Vol 3 (1) ◽  
pp. 362-372
Author(s):  
Svitlana Antoshchuk ◽  
Oksana Babilunha ◽  
Thanh Tran Kim ◽  
Anatolii Nikolenko ◽  
Tien Nguyen Thi Khanh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Prediction ◽  
Network Models ◽  
Stationary Time Series ◽  
Neural Network Models ◽  
One Dimensional ◽  
Stationary Time

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models
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