Improving DWT-RNN Model Via B-spline Wavelet Multiresolution to Forecast a High-Frequency Time Series.

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. (C) 2019 Elsevier Ltd. All rights reserved.