Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series
| Autor: | Farid Mohammad Maalek Ghaini, Faramarz Samavati, Zeinab Hajiabotorabi, Aliyeh Kazemi |
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| Rok vydání: | 2019 |
| Předmět: |
Discrete wavelet transform
0209 industrial biotechnology Artificial neural network Series (mathematics) business.industry Computer science Computer Science::Neural and Evolutionary Computation General Engineering Pattern recognition 02 engineering and technology Computer Science Applications Data set Nonlinear Sciences::Adaptation and Self-Organizing Systems 020901 industrial engineering & automation Wavelet Recurrent neural network Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Expert Systems with Applications. 138:112842 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2019.112842 |
| Popis: | This paper presents a recurrent neural network (RNN) which is improved by using an efficient discrete wavelet transform (DWT) for predicting a high-frequency time series. In the combined DWT-RNN model, first, a multiresolution based on B-spline wavelet of high order d (BSd) is used to decompose the time series into several smooth data sets. Therefore, an approximation data set (with low-frequency) and several detail data sets (with high-frequency), with small wave amplitude, are obtained. Then, all decomposed components are used as RNN inputs. The proposed BSd-RNN model can approximate smooth patterns with satisfactory accuracy, and because of the local properties, BSd is a better choice than other common DWT such as Haar and Daubechies of order n (dbn), for preprocessing the high-frequency time series. According to results of performance metrics for predicting four different stock indices, the BSd-RNN model outperforms other common DWT-RNN model such as Haar-RNN and dbn-RNN. Also, the results show the BSd-RNN model outperforms other common artificial neural network (ANN) model such as multilayer feed-forward neural network (FFNN). Finally, The results show that BS3-RNN predicting model has better predictive ability than other compared models which use other wavelets or other ANNs. |
| Databáze: | OpenAIRE |
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