Comparison of time series forecasting with artificial neural network and statistical approach
In this paper we concentrate on prediction of future values based on the past course of a variable. Traditionally this problem is solved using statistical analysis – first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Mendel University Press
2011-01-01
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| Series: | Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis |
| Subjects: | |
| Online Access: | https://acta.mendelu.cz/59/2/0347/ |
| Summary: | In this paper we concentrate on prediction of future values based on the past course of a variable. Traditionally this problem is solved using statistical analysis – first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The time series modelling is a very powerful method, but it requires knowledge or discovery of initial conditions when constructing the model. The experiment described in this paper consists of a comparison of results computed by Multi-layer perceptron network with different learning algorithms previously published and results computed with different types of ARMA models. For the network configuration an analytical approach has been applied through the cross-validation method. We performed an exact comparison of both approaches on real-world data set. Results of two types of artificial neural network learning algorithms are compared with two algorithms of statistical prediction of future values.The experiment results are later discussed from several different points. First the comparison is focused on output precision of both approaches. The comparison consists of matching neural networks results and real values on few steps of prediction. Then the results of ARMA models are compared with real values and conclusion is made. The conclusion also includes theoretical and practical recommendations. |
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| ISSN: | 1211-8516 2464-8310 |