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A Hybrid Model of Primary Ensemble Empirical Mode Decomposition and Quantum Neural Network in Financial Time Series Prediction.
- Source :
-
Fluctuation & Noise Letters . Aug2023, Vol. 22 Issue 4, p1-14. 14p. - Publication Year :
- 2023
-
Abstract
- Financial time series are nonlinear, volatile and chaotic. Inspired by quantum computing, this paper proposed a new model, called primary ensemble empirical mode decomposition combined with quantum neural network (PEEMD-QNN) in predicting the stock index. PEEMD-QNN takes the advantages of the PEEMD which retains the main component of modal component and QNN. To demonstrate that our PEEMD-QNN model is robust, we used the new model to predict six major stock index time series in China at a specific time. Detailed experiments are implemented for both of the proposed prediction models, in which empirical mode decomposition combined with QNN (EMD-QNN), QNN and BP neural network are compared. The results demonstrate that the proposed PEEMD-QNN model has higher accuracy than BP neural network, QNN model and EMD-QNN model in stock market prediction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02194775
- Volume :
- 22
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Fluctuation & Noise Letters
- Publication Type :
- Academic Journal
- Accession number :
- 169947421
- Full Text :
- https://doi.org/10.1142/S0219477523400060