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Forecasting; Nonlinear time series; Neural networks; Moving averages
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Full-Text Articles in Engineering
Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review, Ahmed Tealab
Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review, Ahmed Tealab
Future Computing and Informatics Journal
This paper studies the advances in time series forecasting models using artificial neural network methodologies in a systematic literature review. The systematic review has been done using a manual search of the published papers in the last 11 years (2006e2016) for the time series forecasting using new neural network models and the used methods are displayed. In the covered period in the study, the results obtained found 17 studies that meet all the requirements of the search criteria. Only three of the obtained proposals considered a process different to the autoregressive of a neural networks model. These results conclude that, …
Withdrawn: Forecasting Of Nonlinear Time Series Using Artificial Neural Network, Amr Badr
Withdrawn: Forecasting Of Nonlinear Time Series Using Artificial Neural Network, Amr Badr
Future Computing and Informatics Journal
When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on …
Forecasting Of Nonlinear Time Series Using Ann, Ahmed Tealab, Hesham Hefny, Amr Badr
Forecasting Of Nonlinear Time Series Using Ann, Ahmed Tealab, Hesham Hefny, Amr Badr
Future Computing and Informatics Journal
When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on …