Forecasting import and export volume with a combined model based on wavelet filtering

2016 ◽  
Vol 14 (03) ◽  
pp. 1650018
Author(s):  
Huidian Long ◽  
Guangle Yan
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
High Frequency ◽  
Low Frequency ◽  
Stationary Time Series ◽  
Combined Model ◽  
Model Based ◽  
Stationary Time ◽  
Export Volume ◽  
Import And Export

In this paper, a non-stationary time series prediction method based on wavelet transform is proposed. By wavelet decomposition, the non-stationary time series is decomposed into a low frequency signal and several high frequency signals. The high frequency signals are predicted with auto-regressive integrated moving average (ARIMA) models, and the low frequency is predicted with an improved GM(1,1)-Markov chain combined model based on Taylor approximation. Finally, an improved ARIMA-GM(1,1)-Markov chain combined model is constructed by using wavelet reconstruction. As an example, we use the statistical data of the total import and export volume in China from 2001 to 2014 for a validation of the effectiveness of the combined model.

scholarly journals Regional distribution of non-human H7N9 avian influenza virus detections in China and construction of a predictive model

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zeying Huang ◽  
Haijun Li ◽  
BeiXun Huang
Keyword(s):  
Influenza Virus ◽  
Avian Influenza ◽  
Avian Influenza Virus ◽  
High Frequency ◽  
Regional Distribution ◽  
Low Frequency ◽  
Early Summer ◽  
Late Spring ◽  
Combined Model ◽  
Model Based

Abstract Introduction H7N9 avian influenza has broken out in Chinese poultry 10 times since 2013 and impacted the industry severely. Although the epidemic is currently under control, there is still a latent threat. Material and Methods Epidemiological surveillance data for non-human H7N9 avian influenza from April 2013 to April 2020 were used to analyse the regional distribution and spatial correlations of positivity rates in different months and years and before and after comprehensive immunisation. In addition, positivity rate monitoring data were disaggregated into a low-frequency and a high-frequency trend sequence by wavelet packet decomposition (WPD). The particle swarm optimisation algorithm was adopted to optimise the least squares support-vector machine (LS-SVM) model parameters to predict the low-frequency trend sequence, and the autoregressive integrated moving average (ARIMA) model was used to predict the high-frequency one. Ultimately, an LS-SVM-ARIMA combined model based on WPD was constructed. Results The virus positivity rate was the highest in late spring and early summer, and overall it fell significantly after comprehensive immunisation. Except for the year 2015 and the single month of December from 2013 to 2020, there was no significant spatiotemporal clustering in cumulative non-human H7N9 avian influenza virus detections. Compared with the ARIMA and LS-SVM models, the LS-SVM-ARIMA combined model based on WPD had the highest prediction accuracy. The mean absolute and root mean square errors were 2.4% and 2.0%, respectively. Conclusion Low error measures prove the validity of this new prediction method and the combined model could be used for inference of future H7N9 avian influenza virus cases. Live poultry markets should be closed in late spring and early summer, and comprehensive H7N9 immunisation continued.

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