Canadian Social Science
Research on the Demand Forecasting Method of Sichuan Social Logistics Based on Positive Weight Combination
Abstract
The macro-social logistics demand forecast is of great strategic significance to optimize the national or regional economic structure, improve the investment environment and improve the overall competitiveness of regional economy. In this study, the total amount of social logistics in Sichuan province was selected to reflect the social logistics demand, the factors influencing the social logistics demand in Sichuan province were analyzed, and eight economic indicators were summarized. This study first USES the time series prediction model (including the time response model GM (1, 1)), an exponential smoothing model, causal relation model (including multidimensional prediction model GM (1, n) and BP neural network model), to build four methods combination model, weight given solution of linear programming each forecast model, the forecasting result of combination forecast model deviation is minimal. The posterior difference test was applied to the above five models to compare the prediction results of each prediction method.
Keywords
Full Text:
PDFReferences
Agostino, N., Antonio, C.(2014). Urban Freight Demand Forecasting: A Mixed Quantity/Delivery/Vehicle-Based Model. Transportation Research Part E, 65.
Feng, Y. Y. (2016). Research on cold chain logistics demand forecasting for agricultural products in Beijing(Master’s thesis). Available from North China electric power university (Beijing).
fu, B., zhang, J. J., zhong, j, huang, c. L., &yu, Z. B. (2017). Application Research of Data Mining Methods Based on BP Neural Network in Demand Forecasting. Intelligence Science, 35(11),132-135.
Gu, C. Y. , luo, X., &cheng, W. L. (2010). Application of Variable Weight Combination Forecasting Model in Short-Term Traffic Flow Prediction. Statistics and Decision-Making, (6),168-169.
He, Y. X., &Liu, N. (2015). Methodology of Emergency Medical Logistics for Public Health Emergencies. Transportation Research Part E, 79.
huang, M. Z. (2012). Forecasting method and case study of regional freight volume. (Master’s thesis). Available from Southwest jiaotong university.
Li, S. Q., ren, X. T., &XU, M. H.(2008). China Logistics Demand Forecast Analysis Based On Path Analysis. Logistics Engineering and Management, 40(1),23-26.
li, X. P. (2017). Agricultural Products Logistics Demand Forecast Based on Gray Linear Combination Model. Journal Of Beijing Jiaotong University (Social Science Edition), 6(1), 120-126.
Lin, R. T., Chen, L. C., li, shao, J., &huang, H. R.(2007). Regional Logistics Demand Forecast Based on Gray Neural Network. Value Engineering, (2),92-95.
Liu, T. J.(2017). Forecast and opportunity analysis of cold chain logistics demand of fresh agricultural products under the integration of Beijing, Tianjin and Hebei. American scientific research press.
Rong, L. Q, &huang, P. H. (2017). Research on the Demand and Influencing Factors of Cold Chain Logistics of Fruits and Vegetables in Guangxi Based on Grey Theory. China Agricultural Resources and Zoning, 38(12), 227-234.
Sun, J. Q. (2016). Research on logistics, demand prediction in Beijing (Master’s thesis). Available from Beijing jiaotong university.
sun, x., liu, b., zhu, H. M., &yao, h. (2008). New Comprehensive Forecasting Method for Urban Logistics Demand Based on Economic Density. Logistics Technology, 37(2),62-67.
wang, L. J. (2016). Shaanxi fruit cold chain logistics demand forecast(Master’s thesis). Available from Taiyuan university of technology.
wang, L. J., zhang, Z. P., &sun, X. X. (2005). Logistics Forecasting Method Based on BP Neural Network. Hoisting Transportation Machinery,(5),30-32.
wang, X. L., &zhao k. (2010). Research on Demand Prediction of Agricultural Products Logistics Based on Neural Network. Agricultural Technology and Economy, (2),62-68.
Yang, J. W. (2011). Regional logistics prediction research based on GM/BP neural network combination prediction model (Master’s thesis). Available from Central south university.
Yang, L. L. (2016). Research on service quality evaluation of food cold-chain logistics based on pca-bp neural network(Master’s thesis). Available from Shenzhen University.
yuan, j. (2017). Forecast of Agricultural Cold Chain Logistics Demand Under the Forward Weight Combination Forecasting Mechanism . Jiangsu Agricultural Science, 45(19),341-346.
Zhao, C. C. (2017) Regional logistics demand forecasting methods and case studies(Master’s thesis). Lanzhou jiaotong university.
DOI: http://dx.doi.org/10.3968/10441
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 Canadian Social Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
Reminder
- How to do online submission to another Journal?
- If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:
1. Register yourself in Journal B as an Author
- Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.
2. Submission
Online Submission: http://cscanada.org/index.php/css/submission/wizard
- Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.
- We only use four mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases: caooc@hotmail.com; office@cscanada.net; ccc@cscanada.net; ccc@cscanada.org
Articles published in Canadian Social Science are licensed under Creative Commons Attribution 4.0 (CC-BY).
Canadian Social Science Editorial Office
Address: 1020 Bouvier Street, Suite 400, Quebec City, Quebec, G2K 0K9, Canada.
Telephone: 1-514-558 6138
Website: Http://www.cscanada.net; Http://www.cscanada.org
E-mail:caooc@hotmail.com; office@cscanada.net
Copyright © Canadian Academy of Oriental and Occidental Culture 
