Adaptive neural network model for time-series forecasting. (English) Zbl 1208.62151
Summary: A novel adaptive neural network \((ADNN)\) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust the forecasting results by the relative error and make them more accurate. The proposed \(ADNN\) method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the autoregression \((AR)\), artificial neural network \((ANN)\), and adaptive \(k\)-nearest neighbors \((AKN)\) models. The \(ADNN\) model is proved to benefit from the merits of the \(ANN\) and the \(AKN\) through its’ novel structure with high robustness particularly for both chaotic and real time-series predictions.
MSC:
| 62M20 | Inference from stochastic processes and prediction |
| 62M45 | Neural nets and related approaches to inference from stochastic processes |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
Keywords:
adaptive metricsReferences:
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