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.

堆叠泛化是一种常用技术，通过使用高级模型组合表达能力较差的模型来提高预测准确性。本文介绍了一种专为非线性时间序列模型设计的堆叠泛化方案。我们的方法不是使用传统的模型选择标准来选择单个模型，而是堆叠了来自不同类别的多个非线性时间序列模型，并提出了一种新的泛化算法，可以最大限度地减少预测误差。为了实现这一目标，我们利用前馈人工神经网络（FANN）模型通过堆叠现有的非线性时间序列模型来概括它们。使用反向传播算法估计网络参数。我们使用模拟示例和真实数据应用来验证所提出的方法。结果表明，与之前的非线性时间序列模型相比，我们提出的堆叠 FANN 模型实现了更低的误差并提高了预测精度，从而更好地拟合原始时间序列数据。