Forecasting Using Elman Recurrent Neural Network
| Autor: | Youssef Masmoudi, Habib Chabchoub, Emna Krichene, Ajith Abraham, Adel M. Alimi |
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| Rok vydání: | 2017 |
| Předmět: |
Scheme (programming language)
Series (mathematics) Artificial neural network business.industry Computer science Computer Science::Neural and Evolutionary Computation 010102 general mathematics 02 engineering and technology 01 natural sciences Regression Recurrent neural network Order (business) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business computer computer.programming_language |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319534794 ISDA |
| DOI: | 10.1007/978-3-319-53480-0_48 |
| Popis: | Forecasting is an important data analysis technique that aims to study historical data in order to explore and predict its future values. In fact, to forecast, different methods have been tested and applied from regression to neural network models. In this research, we proposed Elman Recurrent Neural Network (ERNN) to forecast the Mackey-Glass time series elements. Experimental results show that our scheme outperforms other state-of-art studies. |
| Databáze: | OpenAIRE |
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