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


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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    • "Then, Luna et al. [21], Rojas et al. [22], Wong et al. [23], and Zhao and Yang [24] used the fuzzy clustering and artificial neural network (ANN) to solve the problem of time series forecasting. Aladag et al. [25] proposed a new hybrid approach by combining Elman's recurrent neural networks (ERNN) and ARIMA models to forecast the nonlinear times series like the Canadian Lynx data. Egrioglu et al. [26] constructed a hybrid model of seasonal ARIMA, autoregressive conditional heteroscedasticity (ARCH), and ANN to forecast nonlinear time series. "
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    ABSTRACT: There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.
    Mathematical Problems in Engineering 01/2015; 2015(4):1-10. DOI:10.1155/2015/635345 · 0.76 Impact Factor
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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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