A new approach based on artificial neural networks for high order multivariate fuzzy time series

2009 ◽  
Vol 36 (7) ◽  
pp. 10589-10594 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu ◽  
Vedide R. Uslu ◽  
Murat A. Basaran
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
High Order ◽  
Fuzzy Time Series ◽  
New Approach ◽  
Artificial Neural

scholarly journals A New High Order Fuzzy ARMA Time Series Forecasting Method by Using Neural Networks to Define Fuzzy Relations

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Cem Kocak
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Model Specification ◽  
Moving Averages ◽  
Specification Error ◽  
Artificial Neural

Linear time series methods are researched under 3 topics, namely, AR (autoregressive), MA (moving averages), and ARMA (autoregressive moving averages) models. On the other hand, the univariate fuzzy time series forecasting methods proposed in the literature are based on fuzzy lagged (autoregressive (AR)) variables, having not used the error lagged (moving average (MA)) variables except for only two studies in the fuzzy time series literature. Not using MA variables could cause the model specification error in solutions of fuzzy time series. For this reason, this model specification error should be eliminated. In this study, a solution algorithm based on artificial neural networks has been proposed by defining a new high order fuzzy ARMA time series forecasting model that contains fuzzy MA variables along with fuzzy AR variables. It has been pointed out by the applications that the forecasting performance could have been increased by the proposed method in accordance with the fuzzy AR models in the literature since the proposed method is a high order model and also utilizes artificial neural networks to identify the fuzzy relation.

scholarly journals An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

2018 ◽  
Vol 8 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Esra Akdeniz ◽  
Erol Egrioglu ◽  
Eren Bas ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Real Life ◽  
Life Time ◽  
High Order ◽  
Non Linear ◽  
Artificial Neural

Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

Artificial neural networks and fuzzy time series forecasting: an application to air quality

2014 ◽  
Vol 49 (6) ◽  
pp. 2633-2647 ◽  
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee ◽  
Suhartono ◽  
Mohd Talib Latif
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Artificial Neural
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