A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems
作者：
Highlights：
• We build-up a vector ANN to be exploited as a nonlinear VAR model.
• The scheme is improved with a wavelet-based data preprocessing task.
• A comparison to VAR models from which data were simulated shows its superiority.
• The model is validated for real-world extremely fluctuating data.
• The wavelet-based vector ANN outperforms the ANN-based NAR model.
摘要
•We build-up a vector ANN to be exploited as a nonlinear VAR model.•The scheme is improved with a wavelet-based data preprocessing task.•A comparison to VAR models from which data were simulated shows its superiority.•The model is validated for real-world extremely fluctuating data.•The wavelet-based vector ANN outperforms the ANN-based NAR model.
论文关键词：Bivariate processes,Forecasting,Wavelet coefficients,Feedforward neural networks,Nonlinear autoregressive models,Econophysics
论文评审过程：Available online 28 March 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.03.030
