Prediction Method of Time Series Data Stream Based on Wavelet Transform and Least Squares Support Vector Machine

Kong, Yinghui; Shi, Yancui; Yuan, Jinsha

doi:10.1109/icnc.2008.255

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Object Method Shrouf et al 43 2003 ECG Wavelet þ linear regression Gan et al 44 2005 Nonlinear time series Wavelet þ ANN Yousefi et al 33 2005 Oil prices Wavelet þ data fitting Kong et al 45 2008 Time series Wavelet þ SVM Wang et al 46 2008 Non-stationary time series Wavelet þ AR þ SVM Chang and Fan 34 2008 Stock price Wavelet þ TSK fuzzy Dou et al 30 and Tan et al 35 2009 and 2012 Traffic flow prediction Wavelet þ ARMA Kisi 40 2009 Water quality management Wavelet þ ANN Kuo 38 2010 Seasonal rainfall forecasting Wavelet þ ANN Akyilmaz et al 37 2011 Earth rotation parameters Wavelet þ Fuzzy Zhou et al 41 2011 Solar energy Wavelet þ ANN Li and Wu 39 2012 Nonlinear time series Wavelet þ ANN Moreno et al 47 2012 Electricity demand forecasting Wavelet þ nonlinear AR Jiang 36 2013 Network flow prediction Wavelet þ restrained LAP Yao and Yu 31 2013 Wind speed Wavelet þ ANN Zhao et al 42 2013 Missile spare parts Wavelet þ GM þ ARMA ECG: electrocardiogram; ANN: artificial neural network; AR: auto regressive; SVM: support vector machine; TSK: Takagi-Sugeno-Kang; ARMA: LAP: local area perssad; ARMA: autoregressive moving average; GM: grey model.…”

Section: Yearmentioning

confidence: 99%

A novel prediction model for aircraft spare part intermittent demand in aviation transportation logistics using multi-components accumulation and high resolution analysis

Zhang

Yan

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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High effective management of civil aircraft spare parts is of significant importance for the economical operation of aircrafts. However, the stochastic characteristics of the aircraft spare parts make it difficult to find a reliable rule to precisely predict the future demand. In order to address this issue, this work presents a novel multi-components accumulation and high resolution analysis (MCAHR) method to improve the forecasting performance of aircraft spare parts. The MCAHR takes the advantages of high resolution of the wavelet transform to analyze the time series of spare part intermittent demand. The original time series were decomposed into several sub-bands along with the time axis of the wavelet. Then particle swarm optimized fuzzy neural networks were established for each sub-band to intelligently mine their intrinsic features. Accurate prediction result was hence obtained by the accumulation of the outputs of all fuzzy neural networks. Experimental tests using the historical data of A320 civil aircrafts were carried out in this work to evaluate the proposed MCAHR method. The analysis results have demonstrated a high efficiency of the MCAHR method and that its prediction performance is superior to existing methods. Hence, the proposed MCAHR method has practical importance in the civil aircraft spare part intermittent demand prediction and will provide a significant economic benefit to the industry through reasonable management of aircraft spare parts.

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