Hybrid wavelet-neural network models for time series

Kılıç D. K., Uğur Ö.

APPLIED SOFT COMPUTING, cilt.144, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 144
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.asoc.2023.110469
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Long short-term memory (LSTM), Multiresolution analysis (MRA), Nonlinear models, Recurrent neural network (RNN), Time series analysis, Wavelet neural network (WNN), Wavenet
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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/).