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Journal article
Stacking-Based Neural Network for Nonlinear Time Series Analysis
Statistical methods & applications, Vol.33(3), pp.901-924
07/2024
Web of Science ID: WOS:001165040300001
Abstract
Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model. This paper introduces a stacked generalization scheme specifically designed for nonlinear time series models. Instead of selecting a single model using traditional model selection criteria, our approach stacks several nonlinear time series models from different classes and proposes a new generalization algorithm that minimizes prediction error. To achieve this, we utilize a feed-forward artificial neural network (FANN) model to generalize existing nonlinear time series models by stacking them. Network parameters are estimated using a backpropagation algorithm. We validate the proposed method using simulated examples and a real data application. The results demonstrate that our proposed stacked FANN model achieves a lower error and improves forecast accuracy compared to previous nonlinear time series models, resulting in a better fit to the original time series data.
Details
- Title
- Stacking-Based Neural Network for Nonlinear Time Series Analysis
- Publication Details
- Statistical methods & applications, Vol.33(3), pp.901-924
- Resource Type
- Journal article
- Publisher
- Springer
- Number of pages
- 24
- Copyright
- © 2024, The Author(s), under exclusive licence to Società Italiana di Statistica
- Identifiers
- WOS:001165040300001; 99380581190006600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English