Stacking-based neural network for nonlinear time series analysis

Tharindu P. De Alwis () and S. Yaser Samadi ()
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Tharindu P. De Alwis: Worcester Polytechnic Institute
S. Yaser Samadi: Southern Illinois University Carbondale

Statistical Methods & Applications, 2024, vol. 33, issue 3, No 8, 924 pages

Abstract: 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.

Keywords: Stacked generalization; Cross-validation; Time series; Feed-forward artificial neural network (FANN); Backpropagation algorithm (search for similar items in EconPapers)
JEL-codes: C4 C45 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-024-00746-0

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