Forecasting nonlinear time series with a hybrid methodology

Applied Mathematics Letters (Impact Factor: 1.48). 09/2009; 22:1467-1470. 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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    Jurnal Teknologi. 01/2014; 70(5).
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    ABSTRACT: Time series forecasting has been applied to different applications. On the other hand, Artificial Neural Networks (ANNs) have been widely used in developing time series forecasting models. In this paper, an evolutionary neural network model is proposed to improve the performance of ANNs in time series forecasting. In this way, a Modified Genetic Algorithm (MGA) is used to determine the optimum structure and parameters of an ANN, such as the number of hidden nodes, the slope of nodes' activation function, the values of learning rate and the momentum coefficient in hidden and output layers, and the number of input features. To evaluate the effectiveness of proposed model, Foreign Exchange (FOREX) rate prediction, as a benchmark application, is performed in this paper. Empirical results show that by using suitable operators for selection and crossover operations in MGA, the mean squared error (MSE) of the proposed modified evolutionary-connectionist hybrid model reaches 0.0008, which is an acceptable performance as compared with some other algorithms.
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    ABSTRACT: Load forecasts of short lead times ranging from an hour to a day ahead are essential for improving the economic efficiency and reliability of power systems. This paper proposes a hybrid model based on the wavelet transform (WT) and the weighted nearest neighbor (WNN) techniques to predict the day ahead electrical load. The WT is used to decompose the load series into deterministic series and fluctuation series that reflect the changing dynamics of data. The two subseries are then separately forecast using appropriately fitted WNN models. The final forecast is obtained by composing the predicted results of each subseries. The hourly electrical load of California and Spanish energy markets are taken as experimental data and the mean absolute percentage error (MAPE), Weekly MAPE (WMAPE) and Monthly MAPE (MMAPE) are computed to evaluate the forecasting performance of the next-day load forecasts. The forecasting efficiency of the proposed model is evaluated using db2, db4, db5 and bior 3.1 wavelets. The results demonstrate the forecasting accuracy of the proposed hybrid model.
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