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

作者：

Highlights：

• We apply the reverse subdivision form of BSd for prediction.

• BSd can decompose noisy HV time series into several smooth signals.

• All decomposed components are simultaneously used as new inputs of an RNN model.

• The advantage of applying BSdis shown by comparing it with other common models.

摘要

•We apply the reverse subdivision form of BSd for prediction.•BSd can decompose noisy HV time series into several smooth signals.•All decomposed components are simultaneously used as new inputs of an RNN model.•The advantage of applying BSdis shown by comparing it with other common models.

论文关键词：Discrete wavelet transform,B-spline wavelets multiresolution,Artificial neural networks,Return volatility,Financial time series forecasting

论文评审过程：Received 14 April 2019, Revised 24 July 2019, Accepted 25 July 2019, Available online 25 July 2019, Version of Record 29 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112842