1. Forecasting of Mackey-Glass Time Series Using Elman Recurrent Neural Network
Emna Krichene 1, Youssef Masmoudi 1, Adel M. Alimi1 1, Ajith Abraham 2, Habib Chabchoub 3
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

2. Agenda
Introduction
Forecasting of Mackey-Glass Time Series Using Elman Recurrent Neural Network
Literature review
Contribution
Conclusion
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

3. Information Extraction Predictive analytics information
Introduction |The Forecasting
The Forecasting
Information Extraction
Introduction
Introduction
An important aspect related to forecasting represents the subject of much research today.
Forecasting is an important data analysis technique that aims to study historical data in order to explore and predict its future values.
Various fields to forecast:
The behavior of users
Weather
Earthquakes
Stock market…
Literature review
Contribution
Conclusion
Forecasting
Predictive analytics information
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

4. Agenda
Introduction
Forecasting of Mackey-Glass Time Series Using Elman Recurrent Neural Network
Literature review
Literature review
Contribution
Conclusion
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

5. Extrapolative methods
Forecasting literature contains a large variety of methods that can be classified into two main families:
II.
Quantitative methods
Used when situation is stable & historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions I.
Qualitative methods
Used when situation is vague & little data exist
New products
New technology
Involves intuition, experience
e.g., forecasting sales on Internet
Extrapolative methods
(time series model)
Causal methods
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

6. Agenda
Introduction
Forecasting of Mackey-Glass Time Series Using Elman Recurrent Neural Network
Literature review
Contribution
Proposed Method
Conclusion
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

7. Contribution |Building the architecture
The Learning process consists on :
Estimating the number of hidden neurons.
Estimating and adjusting the alpha learning rate.
Adjusting the connections weights.
Step 1
Step 2
Initial parameters system
Training data set
Learning process
Normalization No
Yes
The optimized built architecture
Yes
Termination criteria met ?
Evaluation
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

8. Figure 1. The architecture of the optimized Elman RNN
Contribution |The optimized RNN architecture
Figure 1. The architecture of the optimized Elman RNN
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

9. Contribution
Serving by the back-propagation learning algorithm, all connection weights in the network are simply computed and updated even the context connections.
Fig. 2 depicts the example of 500 samples from the Mackey-Glass time Series predicted by our network architecture.
Basically, the connection strengths between hidden past values and their corresponding actual ones are fixed to one as we suppose retaining past values without applying any modification; however experiments show that updating connection strengths between context units and hidden units vkj in the same way as wij gives better results than fixing them at 1.
Serving by the back-propagation learning algorithm, all connection weights in the network are simply computed and updated even the context connections.
Fig. 2 depicts the example of 500 samples from the Mackey-Glass time Series predicted by our network architecture. We remark that the proposed scheme gives accurate results as they are predicted efficiently.
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

10. Agenda
Introduction
Forecasting of Mackey-Glass Time Series Using Elman Recurrent Neural Network
Literature review
Contribution
Conclusion
Conclusion
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

11. Conclusion
Several techniques have been proposed in the literature to solve forecasting problem, but none of them can be considered as the best.
The choice of the method can be generalized and dependent on several factors.
RNN can be a good alternative model to handle hard time series as it is well suited to memorize past values to use them as memory within the training phase.
One probable extension is to forecast error values and to combine them to obtain optimized results.
Several techniques have been proposed in the literature from statistical methods to those based on artificial intelligence techniques to solve forecasting problem, but none of them can be considered as the best.
The choice of the method can be generalized and dependent on several factors including the size of the historical data, the complexity of the proposed model, the variance of the error and the complexity of the algorithms i.e the time needed to analyze the data.
RNN can be a good alternative model to handle hard time series as it is well suited to memorize past values to use them as memory within the training phase.
One probable extension is to use data mining techniques and to choose or combine the ones that give the best results.
16 th International Conference on Intelligent Systems Design and Applications (ISDA)

12. Thank you
Emna KRICHENE, Youssef Masmoudi, Adel M. Alimi, Ajith Abraham, Habib Chabchoub 3 ,
16 th International Conference on Intelligent Systems Design and Applications (ISDA)