A new hybrid methodology for nonlinear time series forecasting
A new hybrid methodology for nonlinear time series forecasting
复制标题
非线性时间序列预测的新混合方法
DOI：
10.1155/2011/379121
复制
发表时间：
2011
影响因子：
3.2
通讯作者：
M. Bijari
中科院分区：
文献类型：
--
作者：
M. Khashei;M. Bijari
人工神经网络（ANN）是灵活的计算框架和通用逼近器，可以高精度地应用于各种预测问题。然而，使用 ANN 来模拟线性问题会产生不同的结果，因此；盲目地将它们应用于任何类型的数据都是不明智的。这就是时间序列预测文献中提出了结合 ARIMA 等线性模型和 ANN 等非线性模型的混合方法的原因。尽管结合 ARIMA 和 ANN 的传统方法具有所有优点，但它们有一些假设，如果发生相反的情况，它们的性能将会下降。在本文中，提出了一种新的方法，将 ANN 与 ARIMA 相结合，以克服传统混合方法的局限性，并产生更通用和更准确的混合模型。加拿大 Lynx 数据集的经验结果表明，与传统的混合方法相比，所提出的方法是将线性和非线性模型结合在一起的更有效的方法。因此，它可以作为时间序列预测领域混合的适当替代方法，特别是当需要更高的预测精度时。
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditionalmethodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies. Therefore, it can be applied as an appropriate alternative methodology for hybridization in time series forecasting field, especially when higher forecasting accuracy is needed.