Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

By Salim Lahmiri. 2014

111 citations recorded by the source corpus.

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-frequency) and detail (high-frequency) components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA) model and the random walk (RW) process.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Conditional Time Series Forecasting with Convolutional Neural Networks (2017, background): “…work well on noisy series, by discarding in each subsequent layer the noise and extracting only the meaningful patterns, in this way bearing similarities to neural networks which use wavelet transformed time series (i.e. a split in high-and low-frequency components) as input, see e.g. [1], [17].”
Dilated convolutional neural networks for time series forecasting (2018, background): “…work well on noisy series, by discarding in each subsequent layer the noise and extracting only the meaningful patterns, in this way bearing similarities to neural networks which use wavelet transformed time series (i.e. a split in high-and low-frequency components) as input, see e.g. [1], [17].”