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A novel grey model for urban migrant workers' number predication in China
Abstract
It is very important to forecast the number of urban migrant workers in a city, but various traditional prediction models cannot tackle such situation effectively. To solve this problem, this paper builds a novel grey prediction model based on kernel and information field of interval grey number. Then this proposed model is applied to establish the prediction model of the number of urban migrant worker in Chongqing, China. Finally, this model is employed to forecast the number of urban migrant workers in Chongqing, 2010, and obtain a satisfactory simulation precision. Hence, the research model proposed in this paper has an important significance in both practical applications and theoretical study. Keywords-Grey prediction model, Interval grey number, Kernel and information field, Prediction of the number of urban migrant workers
... There are two common methods for predicting workforce: one is to construct a reasonable prediction model based on the analysis of influencing factors [12][13][14][15][16][17] , and the other is to select different prediction models based on the characteristics of the predicted data to improve the accuracy of the prediction 18,19 .The accuracy of the above Content courtesy of Springer Nature, terms of use apply. Rights reserved modeling methods depends on a large amount of data and the selection of key influencing factors. ...
Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%.
... The theoretical basis of gray system is system theory, and gray system is mainly used as the theoretical research object [7][8][9]. This theory absorbs the ideological theories and research methods of disciplines such as information science, mathematics, and computer science. ...
This article analyzes the characteristics of power payment terminal operation. Grey model theory is used to predict the change trend and specific values of the multi-dimensional status information of the equipment. Firstly, the correlation analysis method is used to extract the core data with lower correlation degree from the massive state data. Secondly, the development trend of the data is predicted by the gray prediction model. Finally, the effectiveness of the gray model is verified by actual field data.
On the basis of dividing the spectrum of scientific and technological endeavors into finer sections, the overall development
of modern science has shown the tendency of synthesis at a higher level. This higher level synthesis has caused the appearance
of the various studies of systems science with their specific methodological and epistemological significance. Systems science
reveals the much deeper and more intrinsic connections and interlockings of objects and events and has greatly enriched the
overall progress of science and technology. Many of the historically difficult problems in different scientific fields have
been resolved successfully along with the appearance of systems science and its specific branches. And because of the emergence
of various new areas in systems science, our understanding of nature and the laws that govern objective evolutions has been
gradually deepened. At the end of the 1940s, there appeared systems theory, information theory, cybernetics. Toward the end
of 1960s and the start of 1970s, there appeared the theory of dissipative structures, synergics, catastrophe, and bifurcations. During the middle and toward the end of the 1970s, there appeared one by one such new transfield and interfiled theories
of systems science as the ultracircular theory, dynamic systems, pansystems, etc.
This article has no abstract
Algorithm of grey numbers and grey algebraic system is the theoretical basis of grey system theory. It is possessed of very important significance in the developing course of grey system theory. A definition for grey “Kernel” has been put forward first. The algorithm axiom, algorithm rules of interval grey numbers and a new algebraic system is built based on the “Kernel” and the degree of greyness of grey numbers. And the properties of the algorithm are studied. Up to now, the algorithm of interval grey numbers have been transformed to algorithm of real numbers. So, the difficult problem for set up algorithm of interval grey numbers have been solved to a certain degree. The algorithm of interval grey numbers could be extended to the case of algorithm of grey algebraic equation, grey differential equation and grey matrix which including interval grey numbers. The algorithm system of interval grey numbers also opened a new passageway for research on grey input-output and grey programming, etc.
A definition of ″Kernel″ is given first. Algorithm axioms, algorithm rules of interval grey numbers and new algebraic systems are built based on the ″Kernel″ and the degree of greyness of grey numbers. And the properties of the algorithm are studied. Up to now, the algorithm of interval grey numbers are transformed to the algorithm of real numbers, so the difficult problem for algorithm of interval grey numbers is solved to a certain degree. The algorithm of interval grey numbers is extended to the grey algebraic equation, grey differential equation and grey matrix algorithm including interval grey numbers. The algorithm system of interval grey numbers is helpful for research on grey input-output and grey programming.
Although the grey forecasting model has been successfully employed in many fields and demonstrated promising results, its prediction results may be inaccurate sometimes. For the purposes of enhancing the predictive performance of grey forecasting model and enlarging its suitable ranges, this paper puts forward a novel grey forecasting model termed NGM model and its optimized model, develops a calculative formula for solving the parameters of the novel NGM model through the least squares method, and obtains the time response sequence of NGM model by using differential equation as a procedure for reasoning. It performs a numerical demonstration on the prediction accuracy of NGM model and its optimized models. As shown in the results, the proposed model and it optimized model can enhance the prediction accuracy. Numerical results illustrate that the proposed NGM model and its optimized model are effective. They are suitable for predicting the data sequence with the characteristics of non-homogeneous exponential law. This work makes important contribution to the enrichment of grey prediction theory.
Grey theory is a truly multidisciplinary and generic theory that deals with systems which are characterized by poor information and /or for which information is lacking. In this paper, a modified grey model, combined with a simple statistical method to determine the model coefficient and a sectional model, by using another variable to modify the original grey prediction model for long‐term forecasting, is proposed. This new method not only can improve the prediction accuracy of the original grey model, but also can make it suitable for long‐term forecasting. Finally, we use power demand forecasting in Taiwan for our case study to test the efficiency and accuracy of the proposed method.
This article has proved that discrete function with homogeneous exponential law will become discrete function with non-homogeneous exponential law if the function with homogeneous exponential law accumulates, while discrete function with homogeneous exponential law is generated by inversely accumulating the discrete function with non-homogeneous exponential law. Based on the error analysis of the model GM(1,1), researchers use the discrete function with non-homogeneous exponential law to fit the accumulated sequence in order to propose a new method for optimizing background value in model GM(1,1). There are plenty of data simulations and models. And it shows that the new model broke through restrictions of using coefficient, and it still improved its matching and prediction precision.
In this paper, the starting conditions of GM model group are studied. By choosing the nth component of X(1) as the starting conditions of the grey differential model, improvements are made on the models. As the new information is fully used, the accuracy of prediction is improved greatly. Therefore, it has high theoretical and practical value.
Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proposes a novel discrete grey forecasting model termed DGM model and a series of optimized models of DGM. This paper modifies the algorithm of GM(1,1) model to enhance the tendency catching ability. The relationship between the two models and the forecasting precision of DGM model based on the pure index sequence is discussed. And further studies on three basic forms and three optimized forms of DGM model are also discussed. As shown in the results, the proposed model and its optimized models can increase the prediction accuracy. When the system is stable approximately, DGM model and the optimized models can effectively predict the developing system. This work contributes significantly to improve grey forecasting theory and proposes more novel grey forecasting models.
The stability and stabilization of a grey system whose state matrix is triangular is studied. The displacement operator and established transfer developed by the author are the indispensable tool for the grey system.
In grey system theory, the studies in the field of grey prediction model are focused on real number sequences, rather than grey number ones. Hereby, a prediction model based on inter-val grey number sequences is proposed. By mining the geometric features of interval grey number sequences on a two-dimensional surface, all the interval grey numbers are converted into real num-bers by means of certain algorithm, and then the prediction model is established based on those real number sequences. The entire process avoids the algebraic operations of grey number, and the prediction problem of interval grey number is usefully solved. Ulti-mately, through an example's program simulation, the validity and practicability of this novel model are verified.
Purpose
– To explore a more effective method to study the information content of grey numbers.
Design/methodology/approach
– An axiomatic approach is used for the measurement of information content of a given grey number.
Findings
– A new definition for information content of grey numbers is introduced. And, relevant results are proven.
Originality/value
– This work fills the vacancy on how to measure the information value contained in a grey number.
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