Evolution Pattern Discovery and Case Study of Logistics Companies Based on Time Series Analysis

Abstract:

Accurate demand forecasts are critical for logistics enterprises to improve the efficiency of their resource usage. Demand forecasts of logistics enterprises are closely related to the overall social environment, local economic development, related industries development, seasonal demand change, and the development of the enterprises. Therefore, a direct analytical model is difficult to obtain. Based on the analysis of the historical data of a typical shipping company, this paper presents a time series prediction model for logistics enterprises in the goods traffic forecasts, and through experimental analysis and comparison study, it found that the method proposed in this paper has higher prediction accuracy. Thus, the proposed model can be used to forecast the demand of the freight transport companies and has a spread value.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 1099-1103
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.1099
• Online since: June 2010
• Authors: Bin Yang, Xian Bing Wu, Zhi Hua Hu
• Keywords: Freight Shipping Company, GM(1,1), Logistics Demand Forecasting, Prediction, Time Series Analysis

References

[1] Wang Z., Building grey model GM(1，1) with translation transformation．Kybernetes，2004, 33(2): 390-398.

[2] Sherif I. M., Haitham A. -D., Performance Evaluation of Short-Term Time-Series Traffic Prediction Model．Journal of Transportation Engineering, 2002, 128(6): 490-498.

[3] Qiu T. ．Application of Grey Model in Fire Prediction, information Technology and Application, 2009, 3(1): 429-432.

[4] Toth E., Montanari A. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology, 2000, 239(4): 132-147.

DOI: 10.1016/s0022-1694(00)00344-9

[5] Wang Qi. -J., Hybrid grey model to forecast monitoring series with seasonality, Journal of central south university of technology, 2005, 12(5): 624-627.