Reducing forecasting error under hidden markov model by recurrent neural networks
| Autor: | Tzu-Ting Kao, 高子庭 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 In recent year, artificial neural networks became a very popular machine learning method since it’s high levels performance. So we want to combine neural networks and traditional statistical model and give the method which can catch the advantage of both method. Here the statistical model we are interested is the hidden markov model, and the artificial neural networks we choose is recurrent neural networks. Since we have proved recurrent neural networks output can approximate a posterior probability in classification task, so we put this probability into training process of hidden markov model to improve the accuracy of parameters estimator. The advantage of this algorithm is that we change the original training algorithm from unsupervised to supervised, so we can take the information about data level into training process. The simulation and real data analysis show that this combination training process can not only improve accuracy of parameter estimation and reduce standard error of parameter estimation. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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