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 emna.krichene.tn@ieee.org, youssef.masmoudi@gmail.com, adel.alimi@ieee.org, ajith.abraham@ieee.org, habib.chabchoub@gmail.com 16 th International Conference on Intelligent Systems Design and Applications (ISDA)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

Thank you Emna KRICHENE, Youssef Masmoudi, Adel M. Alimi, Ajith Abraham, Habib Chabchoub 3 , adel.alimi@ieee.org E-mail: emna.krichene.tn@ieee.org, youssef.masmoudi@gmail.com, adel.alimi@ieee.org, ajith.abraham@ieee.org, habib.chabchoub@gmail.com 16 th International Conference on Intelligent Systems Design and Applications (ISDA)