Research on combination forecast of port cargo throughput based on time series and causality analysis

Zhang, Chi; Huang, Lei; Zhao, Zhichao Article Research on combination forecast of port cargo throughput based on time series and causality analysis Journal of Industrial Engineering and Management (JIEM) Provided in Cooperation with: The School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC) Suggested Citation: Zhang, Chi; Huang, Lei; Zhao, Zhichao (2013) : Research on combination forecast of port cargo throughput based on time series and causality analysis, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 6, Iss. 1, pp. 124-134, https://doi.org/10.3926/jiem.687 This Version is available at: https://hdl.handle.net/10419/188513 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ Journal of Industrial Engineering and Management JIEM, 2013 – 6(1):124-134 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.687 - 124 - Research on combination forecast of port cargo throughput based on time series and causality analysis Chi Zhang, Lei Huang, Zhichao Zhao School of Economics and Management, Beijing Jiaotong university (China) [email protected], [email protected], [email protected] Received August 2012 Accepted January 2013 Abstract: Purpose: The purpose of this paper is to develop a combined model composed of greyforecast model and Logistic-growth-curve model to improve the accuracy of forecast model of cargo throughput for the port. The authors also use the existing data of a current port to verify the validity of the combined model. Design/methodology/approach: A literature review is undertaken to find the appropriate forecast model of cargo throughput for the port. Through researching the related forecast model, the authors put together the individual models which are significant to study further. Finally, the authors combine two individual models (grey-forecast model and Logistic-growthcurve model) into one combined model to forecast the port cargo throughput, and use the model to a physical port in China to testify the validity of the model. Findings: Test by the perceptional data of cargo throughput in the physical port, the results show that the combined model can obtain relatively higher forecast accuracy when it is not easy Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 125 - to find more information. Furthermore, the forecast made by the combined model are more accurate than any of the individual ones. Research limitations/implications: The study provided a new combined forecast model of cargo throughput with a relatively less information to improve the accuracy rate of the forecast. The limitation of the model is that it requires the cargo throughput of the port have an Sshaped change trend. Practical implications: This model is not limited by external conditions such as geographical, cultural. This model predicted the port cargo throughput of one real port in China in 2015, which provided some instructive guidance for the port development. Originality/value: This is the one of the study to improve the accuracy rate of the cargo throughput forecast with little information. Keywords: cargo throughput, combined forecast model, Logistic growth curve model, Gray forecast model 1. Introduction By the impact of the continuing recession of the global economy, the trend of global port cargo throughput growth presented a significant slowdown in 2011. Compared with 14% growth rate in 2010, the global port cargo throughput was only about 7.3% growth rate in 2011, which reveal a large decline in global economy. Compared to most ports struggling to survive, the ports in China continued to maintain strong. Chinese port occupied seven seats among the first ten cargo throughput ports. Shanghai Port and Ningbo-Zhoushan Port are sustained the first and second largest cargo throughput port in the whole world. Cargo throughput is very important for a port, it is not only the most basic production index for measuring the port development, but also a significant reference to organize its production, make its development plans and construction. In the meantime, the amount of the cargo throughput may reflect the economic situation and the development level of the port city. Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 126 - Cargo throughput forecast is a significant content of the port development strategy. The correctness and rationality of the forecast means much to various aspects in the development of ports including the scientific port layout, the scale of investment in infrastructure, business strategy, development strategy and the collection and distribution of integrated transport plan. This paper studies the forecast model of cargo throughput of the port, and then takes one port in China as an example to verify the model accuracy, in order to provide a strong reference to the port cargo throughput forecasts. 2. Analysis of current cargo throughput forecast model Methods to predict the cargo throughput of the port can be summarized into two categories: qualitative and quantitative forecast methods. Qualitative forecast methods primarily use of historical data, and rely on personal experience, knowledge and analysis ability to predict the future development. It includes Delphi Method and the method of Survey on the Supply of Goods. Qualitative forecast methods have some defects such as the predict process lack of objectivity and the forecast are broad-brush. While quantitative forecast methods which built up by using mathematical statistical methods to predict the status of future is based on historical statistics. Owing to the more objective and meticulous feature quantitative forecast methods have, this paper would focus on a detailed classification and presentation of quantitative forecast methods. Through the use of statistical methods and mathematical models, quantitative forecast methods based on accurate, timely, systematic, comprehensive survey of statistical data and economic information can predict the future of the port in size, level, speed and other variables (Wang & Yang, 2007; Xu, 2010). It is closely related to statistics, so quantitative forecast methods also known as the statistical prediction. Depending on the different mathematical methods used, quantitative forecast methods can be divided into three categories: time series, causality analysis, and combined forecast method. 2.1. Time series method Time series method is to find out the variation law of the total port cargo throughput of historical data to establish a mathematical model to forecast. Such methods include: moving average, exponential smoothing, gray system, the seasonal changes, autoregressive method and time-independent variable regression analysis (Xu, 2011) method. For example, Jiang and Lei (2009) proved that nonlinear gray model is better than gray model to forecast the cargo throughput of Lianyungang Port; Xu (2011) used autoregressive forecast model to forecast the Shanghai Port cargo throughput. Guo, Song and Ye (2005) shows that the grey Verhulst model on time series error corrected is applicable. When the cargo throughput increases according to Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 127 - the curve with S type, not only higher forecasting accuracy can be obtained, but also the superiority and the features of grey system model can be reserved. 2.2. Causality analysis method Causality analysis methods first identify the relationship between the cargo throughput and some hinterland economic indicators, and then establish the model based on these indicators. Finally this model will forecast the future of the port cargo throughput. Such methods include regression analysis, the coefficient of elasticity, the system dynamics method and the method of neural network (Lin & Chen, 2008). For instance, Huang, Cai and Yi (2010) took LevenbergMarquardt algorithm into BP neural network, and then applied it to the port cargo throughput forecast. He found out forecast accuracy improved greatly by the Levenberg-Marquardt optimization model considering the factors of the national economy. Chen and Chen (2009) achieved promptly the accurate and satisfactory results in the Nanjing cargo throughput forecast with the BP Neural Network. Xu, Yan and Zhang (2006) made sure that the proposed model based on System Dynamics was verified to be effective and feasible by simulation using relevant statistical data of port volume of Ningbo City. 2.3. Combined forecast method Combined forecast method doesn’t directly use of the historical and current data modeling, but establishes an appropriate combination of various kinds of individual forecast models to obtain the optimal (Zhang, 2007) value to forecast the port cargo throughput. For example, Chen and Gu (2010) combined gray model with the linear regression model which proved better than other alternatives. Wu and Feng (2011) first forecast the port cargo throughput with gray model, and then optimized the result by the Markov model in order to improve the accuracy of forecast. 3. The selection of combination forecast model After the introduction above, we can easily find the combination forecast model can reduce the error of a single prediction model (Gao, 2008) to a larger extent. Some errors come from the reality of environmental factors, while others may exist problems in the process of modeling, so that would affect the accuracy of the model in predicting. This paper will make an organic combination of gray forecast model and Logistic population projection model, to achieve more accurate port forecast model. Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 128 - 3.1. Grey forecast model The gray system theory (Deng, 2002; Sun & Zheng, 2007) is the study of how to solve the gray system analysis, modeling, forecasting, decision-making and control. It has been put forward and developed by Professor Deng Julong, Huazhong University of Science and Technology from 1982. In nearly three decades, this model has not only aroused the concern of many scholars, but also been developed by leaps and bounds. At present, it has become one of important method to forecast, decision-make, evaluate, plan and control, systems analyze and model in areas of society, economy, science and technology. Particularly, the gray system has a unique effect in analysis and modeling for short time series, statistics, and incomplete information systems, so it has been widely used. Currently some of the commonly used forecast methods (such as regression analysis, etc.) require a larger number of samples. If the sample size is smaller, it often results in large deviation, which will make the forecast target fail. In the meantime, gray forecast model need less information and it is so convenient to operate that it becomes an effective tool to deal with small-sample forecast. It has been widely used in various fields for high-precise modeling. The forecast is according to the law of development of objective things, past and present, by means of scientific methods to describe and analyze future trends and conditions. Finally the forecast makes the formation of the scientific assumptions and judgments. Gray Forecast Model, abbreviated as GM (1, 1) (Liu, Jiang & Zhang, 2007), is as follows: Firstly, there is an original sequence denoted by x(0), it is made up of x(0)(1), x(0)(2), …, x(0)(n). Then the original sequence is accumulated to obtain the generated sequence. (1) The corresponding differential equation is that: (2) “a”, “u” in the formula (2) are the undetermined coefficients sequences. We can arrange sequences as follows: uax dt dx  )1( )1(   i j jxix 1 )0()1( )()( Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 129 - )( ˆ )()( )0()0( ixixie  (3) (4) (5) According to the formula (4), (5), we can calculate the value of “a” and “u”, and then take them into the differential equation (2), so that the solution of the gray forecast model is: (6) The gray theory doesn’t establish original data model, but generate data model, so the gray theory forecast data does not come directly from the generated, but from the restored ones. We can restore the data in the formula (7). (7) In the formula (7), the equation of the gray forecast error is: , while the mean error is . 3.2. Logistic growth curve model Logistic curve model was first proposed by mathematician P.F.Veihulot in the study of the law of population growth. The model is characterized by a slow growth at first, then grows rapidly in a range, and slows down again after a certain limit reach, the curve presents as slightly elongated "S" shape. This is similar to the law of development of Chinese ports. Since reform and development in China, the economic growth continues, while the import and export volume and the domestic turnover has a huge upgrade in the port industry. However, after the 2008, influenced by the financial crisis, the development of the port industry in China also encountered a bottleneck. Cargo throughput of the port, as an important indicator to directly a u at a uextx   ))1(()1( ˆ)0()1( )( ˆ )1( ˆ )1( ˆ)1()1()0( txtxtx  N TT YBBB u a a1 )( ˆ                           1)]()1([ 1)]3()2([ 1)]2()1([ )1()1( 2 1 )1()1( 2 1 )1()1( 2 1 nxnx xx xx B                )( )3( )2( )0( )0( )0( nx x x YN  n i niee )( 1 Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 130 - reflect the changes in the economic development of the port hinterland, just makes a good illustration of this trend. It is similar as what Logistic curve (Zhu, 2003) model wants to express, so we select this model as a reference. Logistic curve equation is: (8) In the equation (8), “t” stands for the point of the time sequence, generally natural numbers; “a”, “m” is the undetermined coefficient; “k” is a given saturation value by the forecasters according to the actual situation and the growing trend of “y”. 3.3. Combined forecast model The greatest concern in the combined forecast (Ma, Wang & Li, 2009) is how to calculate the weighted mean coefficient to make the combined forecast model to improve the prediction accuracy. Suppose “m”, “n” is the coefficient, “m” stands for how many ways to forecast the cargo throughput, while “n” stands for how many time periods we use. yij(i=1,2,…,n, j=1,2,… ,m) means the predictive value in the “j”th way and the “i”th time period. “rj” stands for the weight of the different forecast methods, it must be met .   m jijji yry 1 0 Suppose yi0 as the combined predictive value in the “i”th time period, so that    m j j r 1 1 . Then establish the nonlinear (Li, Chen & Cui, 2008) programming in order to get minimum mean square error (MSE) as the objective function value: (9) While: (10) at me k y  1 n yyr MSE n i m jijij 2 1 1 )( min                       0, 1 1 ijj m jj yr r Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.687 - 131 - We can simplify the objective function as: (11) MSEj stands for the unbiased variance of the “j”th way. 4. A Port cargo throughput forecast A Port in Southern China is an omnibus advocate hub port, driven by the rapid economic development of hinterland; the port cargo throughput continues to grow continuously. In 1999, the port cargo throughput exceeded 100 million tons, which made it become the second port into “the one hundred million tons club” in China. Since then, the port developed step by step. In 2011, the port cargo throughput reached 451 million tons, among the global port 4. The Port has 50 million-ton berths; 14 million-ton loading and unloading buoy; 23 million-ton loading and unloading of anchorage. Considered from any factors like the historical, economic and cultural, the Port is a representative in the ports of China and the world. With the Port cargo throughput of nearly 11 years as the sample, this paper studies the port cargo throughput prediction on combination forecasting model. year 2002 3 4 5 6 7 8 9 10 11 CT (Mt) 1532 1719 2152 2504 3028 3432 3470 3639 4110 4510 Table 1. The port cargo throughput of nearly 11 years (CT represents Cargo Throughput) 4.1. Establish GM (1, 1) model According to the Port historical data, we can calculate through formula (1) for gray forecast model parameters: a = -0.1, u = 1567.7, while we can also get the small error probability is 1, variance is 0.1906. These two values indicate that the model accuracy for A, so that we can use this result as forecast model: (12) 4.2. Establish Logistic growth curve model Setting the initial value[x,y]= [1,18] in formula (2), we can get the undetermined coefficient “m” equals 4.225, “a” equals 0.2574. Finally, the Logistic growth curve equation is: mqpqp n yyyy M n iiqiipi pq ,,2,1,,, ))(( 1       qp pqqp m jjj MrrMSErMSE 1 2 min 7.15671.0 )1( )1(  x dt dx