Forecasting nonlinear time series with a hybrid methodology

Applied Mathematics Letters (Impact Factor: 1.34). 09/2009; 22(9):1467-1470. DOI: 10.1016/j.aml.2009.02.006
Source: DBLP

ABSTRACT a b s t r a c t In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elman's Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy.

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Available from: Cem Kadilar, Sep 28, 2015
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    • "In all previous works [3] [5] [7], the fi rst stage consists of obtaining the specifi cation of a SARIMA model. In the second stage, the parameters (and fi tted residuals) of the SARIMA model remain fi xed while only the parameters of the artifi cial neural network are estimated. "
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    ABSTRACT: The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artificial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron models.
    Dyna (Medellin, Colombia) 08/2013; 80(180):4-8. · 0.22 Impact Factor
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    • "El pronóstico del modelo es obtenido sumando el pronóstico del valor actual de la serie (que es calculado usando el modelo ARIMA) y el pronóstico del error usando la red neuronal artificial. • Aladag et al. [18] usan la misma aproximación que en [17], pero cambiando el perceptrón multicapa por una red neuronal de Elman. • Tseng et al. [19] usan un modelo SARIMA para capturar la componente lineal de los datos; luego, aplican una red neuronal artificial tipo perceptrón multicapa que recibe como entradas los pronósticos y los residuos del modelo SARIMA. "
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    ABSTRACT: Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales. Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. The proposed model is used for forecasting two benchmark time series; we found that the proposed model is able to forecast the time series with more accuracy that other traditional approaches.
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    • "Numerical results from Table 1 show that both Zhang's hybrid model and Aladag's hybrid model by using the Zhang's methodology outperform the autoregressive integrated moving average (ARIMA) and feedforward neural network (FNN) models for Canadian lynx data. Moreover, Aladag et al. [12] can make a more accurate hybrid model, by replacing the feedforward neural network (FNN) model by Elman's recurrent neural network (ERNN) for Canadian lynx data. However, according to the obtained results, it can be seen that using the proposed hybrid methodology instead of using the Zhang's hybrid methodology for combination improves the accuracy in both Zhang's model and Aladag's model. "
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    ABSTRACT: 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 traditional methodologies 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.
    Modelling and Simulation in Engineering 01/2011; 2011(4). DOI:10.1155/2011/379121
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