Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks
This paper presents a forecasting model that integrates the discrete wavelet transform (DWT) and backpropagation neural networks (BPNN) for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-freq...
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| Format: | Article |
| Language: | English |
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Elsevier
2014-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157813000931 |