Hybrid wavelet-neural network models for time series

The use of wavelet analysis contributes to better modeling for financial time series in the sense of both frequency and time. In this study, S & P500 and NASDAQ data are separated into several components utilizing multiresolution analysis (MRA). Subsequently, using an appropriate neural network structure, each component is modeled. In addition, wavelets are used as an activation function in long short-term memory (LSTM) networks to form a hybrid model. The hybrid model is merged with MRA as a proposed method in this paper. Four distinct strategies are employed: LSTM, LSTM+MRA, hybrid LSTM-Wavenet, and hybrid LSTM-Wavenet+MRA. Results show that the use of MRA and wavelets as an activation function together reduces the error the most.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Wavelet analysis is utilized to separate S & P500 and NASDAQ data into components and model each component using an appropriate neural network structure. Additionally, wavelets are used as an activation function in long short-term memory networks to create a hybrid model. The results demonstrate that employing multiresolution analysis and wavelets as an activation function can significantly reduce the error.