Forecasting nonlinear time series with neural network sieve bootstrap

Giordano, Francesco; Rocca, Michele La; Perna, Cira

doi:10.1016/j.csda.2006.03.003

Cited by 59 publications

(22 citation statements)

References 19 publications

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“…The basic idea of this bootstrap method is to approximate the error process by an AR model of order increasing with the sample size. The sieve bootstrap has been successfully employed to test for an autoregressive unit root (Psaradakis, 2001(Psaradakis, , 2003Chang, 2004), to resample from cointegrating regressions (Chang et al, 2006), to conduct inference with VAR models (Inoue and Kilian, 2002a) and to construct prediction intervals for nonlinear time series using neural networks (Giordano et al, 2007). We develop two resampling algorithms that build both on the …xed regressor bootstrap of Hansen (2000) and on the restricted residuals approach of Nankervis and Savin (1996).…”

Section: Introductionmentioning

confidence: 99%

Sieve bootstrap -tests on long-run average parameters

Fuertes

2008

Computational Statistics & Data Analysis

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractPanel estimators can provide consistent measures of a long-run average parameter even if the individual regressions are spurious. However, the t-test on this parameter is fraught with problems because the limit distribution of the test statistic is nonstandard and rather complicated, particularly in panels with mixed (non)stationary errors. A sieve bootstrap framework is suggested to approximate the distribution of the t -statistic. An extensive Monte Carlo study demonstrates that the bootstrap is quite useful in this context.

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Section: Introductionmentioning

confidence: 99%

Sieve bootstrap -tests on long-run average parameters

Fuertes

2008

Computational Statistics & Data Analysis

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“…One of these areas is time series prediction [6][7][8][9][10][11][12][13][14][15][16]. The major advantage of neural networks is their flexible capability in nonlinear modeling [17].…”

Section: Introductionmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.

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“…However, such an assumption usually failed to be well satisfied given with the poor generalization ability of generic neural network due to the uncertainty of practical data. In addition, a network ensemble that combined a number of neural networks was proposed based on the bootstrap technique presented in (Lee et al 2011;Shrivastava and Panigrahi 2013;Giordano et al 2007), which was claimed to be more stable than those of single networks. While the modeling complexity of the network ensemble probably lead to higher computational cost compared to that of a single network and an uncertain modeling quality ).…”

Section: Introductionmentioning

confidence: 99%

Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks

et al. 2016

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Reliable prediction intervals (PIs) construction for industrial time series is substantially significant for decision-making in production practice. Given the industrial data feature of high level noises and incomplete input, a high order dynamic Bayesian network (DBN)-based PIs construction method for industrial time series is proposed in this study. For avoiding to designate the amount and type of the basis functions in advance, a linear combination of kernel functions is designed to describe the relationships between the nodes in the network, and a learning method based on the scoring criterion-the sparse Bayesian score, is then reported to acquire suitable model parameters such as the weights and the variances. To verify the performance of the proposed method, two types of time series which are the classical Mackey-Glass data mixed by additive noises and a real-world industrial data are employed. The results indicate the effectiveness of our proposed method for the PIs construction of the industrial data with incomplete input.

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Forecasting nonlinear time series with neural network sieve bootstrap

Cited by 59 publications

References 19 publications

Sieve bootstrap -tests on long-run average parameters

Sieve bootstrap -tests on long-run average parameters

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks

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