Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series

Abstract

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 …