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The Optimal Combination Forecasting Based on ARIMA, VAR and SSM

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DOI: 10.23977/acss.2016.11003 | Downloads: 74 | Views: 6683

Author(s)

Mingyan Jiang 1, Beibei Chen 1

Affiliation(s)

1 School of Information Science and Engineering, Shandong University, Jinan, China, 250110

Corresponding Author

Mingyan Jiang

ABSTRACT

In order to overcome the defects of a single time series forecasting model and to improve the prediction accuracy, this paper proposes an improved optimal combination model with Artificial Bee Colony algorithm used to solve the optimal weight coefficient automatically. Taking the Manufacturers' Shipments as an example to analyze, we use ARIMA、VAR and SSM to forecast the shipments respectively. Based on these three models,we construct the optimal combination forecasting model. By inspection, it is superior to the other three models in accuracy. 

KEYWORDS

The optimal combination forecasting model; Artificial Bee Colony algorithm; the Manufacturers’ Shipments; ARIMA; VAR; SSM

CITE THIS PAPER

Beibei, C. and Mingyan, J. (2016) The Optimal Combination Forecasting Based on ARIMA,VAR and SSM. Advances in Computer, Signals and Systems (2016) 1: 13-17.

REFERENCES

[1] Claire, Hongyu Pan, et al. Analysis and Application of Time Series: R language [M]. Beijing: Mechanical Industry Press, 2011.
[2] Karamouz M., Araghinejad sh. Advance Hydrology [M]. Amirkabir University of Technology Press, 2012.
[3] Shiyu Li, Fei Zhang, Zhenglin Wang. Data Analysis: real R language [M]. Beijing: Electronic Industry Press, 2014.
[4] Chengfang Fan, Jianmin Shi. Analysis of Grain Production Cost Forecast based on Holt-Winters and Trend ARMA Combined Model—Taking Corn and Wheat of Shandong Province for Example [J]. Chinese Journal of Agricultural Resources and Regional Planning, vol.3, no.3, pp.45-51, 2014.
[5] Li Zheng, Fengbin Lu, Dongmei Duan, etc. Integration forecast of Chinese pork consumption demand—Empirical based on ARIMA、VAR and VEC models [J]. Systems Engineering— Theory & Practice, vol.4, no.4, pp.918-925, 2013.
[6] Mingyan Jiang, Dongfeng Yuan. Artificial Bee Colony algorithm and its applications [M]. Beijing: Science press,  2014. 

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