E3S Web Conf.
Volume 73, 2018The 3rd International Conference on Energy, Environmental and Information System (ICENIS 2018)
|Number of page(s)||4|
|Section||System Information and Decision Support System|
|Published online||21 December 2018|
Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang - Indonesia
* Corresponding author: hasbiyasinfgilive.undip.ac.id
Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each number of neurons in hidden layer, the looping process is performed several times in order to get the best result. The best one is chosen by the least of Mean Absolute Percentage Error (MAPE) criteria. In this study, the model is applied in the two series of stock price data from Indonesia Stock Exchange. Evaluation of VAR-NN performance was based on train-validation and test-validation sample approach. Based on the empirical stock price data it can be concluded that VAR-NN yields perfect performance both in in-sample and in out-sample for non-linear function approximation. This is indicated by the MAPE value that is less than 1% .
Key words: VAR / FFNN / VARNN / Stock Price
© The Authors, published by EDP Sciences, 2018
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