Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

2017 ◽  
Vol 47 (3) ◽  
pp. 1133-1147 ◽  
Author(s):  
Ozge Cagcag Yolcu ◽  
Eren Bas ◽  
Erol Egrioglu ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Autoregressive Coefficient ◽  
Time Series Modelling ◽  
Artificial Neural

scholarly journals A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ozge Cagcag Yolcu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Clustering ◽  
Neuron Model ◽  
Fuzzy Relation ◽  
Optimization Techniques ◽  
Fuzzy Time Series ◽  
Target Values ◽  
Artificial Neural

Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

scholarly journals Comparison of time series forecasting with artificial neural network and statistical approach

2011 ◽  
Vol 59 (2) ◽  
pp. 347-352 ◽  
Author(s):  
Michael Štencl ◽  
Ondřej Popelka ◽  
Jiří Šťastný
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Learning Algorithms ◽  
Statistical Prediction ◽  
Arma Models ◽  
Data Set ◽  
Time Series Modelling ◽  
Artificial Neural

In this paper we concentrate on prediction of future values based on the past course of a variable. Traditionally this problem is solved using statistical analysis – first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The time series modelling is a very powerful method, but it requires knowledge or discovery of initial conditions when constructing the model. The experiment described in this paper consists of a comparison of results computed by Multi-layer perceptron network with different learning algorithms previously published and results computed with different types of ARMA models. For the network configuration an analytical approach has been applied through the cross-validation method. We performed an exact comparison of both approaches on real-world data set. Results of two types of artificial neural network learning algorithms are compared with two algorithms of statistical prediction of future values.The experiment results are later discussed from several different points. First the comparison is focused on output precision of both approaches. The comparison consists of matching neural networks results and real values on few steps of prediction. Then the results of ARMA models are compared with real values and conclusion is made. The conclusion also includes theoretical and practical recommendations.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

scholarly journals An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

2018 ◽  
Vol 8 (9) ◽  
pp. 1613 ◽  
Author(s):  
Utku Kose
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Prediction ◽  
Optimization Technique ◽  
Mental States ◽  
Target Time ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Artificial Neural

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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