A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

2010 ◽  
Vol 81 (4) ◽  
pp. 875-882 ◽  
Author(s):  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu ◽  
Erol Egrioglu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Model Based ◽  
Adaptive Expectation ◽  
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.

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

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

Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks

2006 ◽  
Vol 38 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Luis Oliva Teles ◽  
Vitor Vasconcelos ◽  
Luis Oliva Teles ◽  
Elisa Pereira ◽  
Martin Saker ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
Cyanobacteria Blooms ◽  
Artificial Neural ◽  
Douro River
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