A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
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DOI：
10.1016/j.asoc.2010.10.015
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发表时间：
2011-03-01
影响因子：
8.7
通讯作者：
Bijari, Mehdi
中科院分区：
文献类型：
--
作者：
Khashei, Mehdi;Bijari, Mehdi
提高预测尤其是时间序列预测的准确性是许多领域的决策者面临的一项重要而又困难的任务。理论和实证研究结果都表明，整合不同的模型可以是一种有效的方法，提高其预测性能，特别是当组合的模型是非常不同的。人工神经网络（ANN）是灵活的计算框架和通用近似器，可以应用于各种预测问题，具有很高的准确度。然而，使用人工神经网络来建模线性问题产生了混合的结果，因此，盲目地将人工神经网络应用于任何类型的数据都是不明智的。自回归积分滑动平均（ARIMA）模型是时间序列预测中最常用的线性模型之一，在过去的十年中，它被广泛应用于构造更精确的混合模型。虽然，混合技术，将时间序列分解成其线性和非线性分量，最近已被证明是成功的单一模型，这些模型有一些缺点。本文提出了一种新的混合人工神经网络和ARIMA模型，以克服上述的局限性的人工神经网络和产生更一般和更准确的预测模型比传统的混合ARIMA-ANN模型。在我们提出的模型中，ARIMA模型在线性建模中的独特优势被用来识别和放大数据中现有的线性结构，然后使用神经网络来确定一个模型来捕获底层数据生成过程并使用预处理数据进行预测。三个著名的真实的数据集的实证结果表明，该模型可以有效地提高预测精度实现传统的混合模型，也单独使用的组件模型。(C)2010 Elsevier B. V.保留所有权利。
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. 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 ANNs blindly to any type of data. Autoregressive integrated moving average (ARIMA) models are one of the most popular linear models in time series forecasting, which have been widely applied in order to construct more accurate hybrid models during the past decade. Although, hybrid techniques, which decompose a time series into its linear and nonlinear components, have recently been shown to be successful for single models, these models have some disadvantages. In this paper, a novel hybridization of artificial neural networks and ARIMA model is proposed in order to overcome mentioned limitation of ANNs and yield more general and more accurate forecasting model than traditional hybrid ARIMA-ANNs models. In our proposed model, the unique advantages of ARIMA models in linear modeling are used in order to identify and magnify the existing linear structure in data, and then a neural network is used in order to determine a model to capture the underlying data generating process and predict, using preprocessed data. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid models and also either of the components models used separately. (C) 2010 Elsevier B.V. All rights reserved.