Mobile communication service income prediction method based on grey buffer operator theory

2014 ◽  
Vol 4 (2) ◽  
pp. 250-259 ◽  
Author(s):  
Pinpin Qu
Keyword(s):  
Mobile Communication ◽  
Operator Theory ◽  
Prediction Models ◽  
Prediction Method ◽  
Grey System Theory ◽  
Telecommunication Service ◽  
Communication Service ◽  
Content Type ◽  
Model Based ◽  
The Future

Purpose – The mobile communication industry in China is vulnerable to competition, industry regulation, macroeconomy and so on, which leads to service income's volatility and non-stationarity. Traditional income prediction models fail to take account of these factors, thus resulting in a low precision. The purpose of this paper is to to set up a new mobile communication service income prediction model based on grey system theory to overcome the inconformity between traditional models and qualitative analysis. Design/methodology/approach – At first, mobile telecommunication service income is divided into number of users (NU) and average revenue per user (ARPU) prediction, respectively. Then, grey buffer operators are introduced to preprocess the time series according to their features and tendencies to eliminate the effect of shock disturbance. As a result, two grey models based on GM(1, 1) are constructed to forecast NU and ARPU, and thus the service income is obtained. At last, a case on Zhujiang mobile communication company is studied. The result proves that the proposed method is not only more accurate, but also could discover the turning point of income. Findings – The results are convincing: it is more effective and accurate to employ grey buffer operator theory to predict the mobile communication service income compared with other methods. Besides, this method is applicable to cases with less data samples and faster development. Practical implications – It's common to come across a system with less data and poor information. At this case, the grey prediction method exposed in the paper can be used to forecast the future trend which will give the predictors advice to achieve fine outcomes. Buffer operators can reduce the effect of shock disturbance and the GM(1, 1) model has the advantages of exploiting information using only a couple of data. Originality/value – Considering the fast development of China's mobile communication in recent years, only limited data can be acquired to predict the future, which will definitely reduce the prediction precision using traditional models. The paper succeeds in introducing GM(1, 1) model based on grey buffer operators into the income prediction and the outcome proves that it has higher prediction precision and extensive application.

scholarly journals Evaluating the Factors Affecting QoE of 360-Degree Videos and Cybersickness Levels Predictions in Virtual Reality

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1530
Author(s):  
Muhammad Shahid Anwar ◽  
Jing Wang ◽  
Sadique Ahmad ◽  
Asad Ullah ◽  
Wahab Khan ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Prediction Models ◽  
Safety Concern ◽  
Prediction Method ◽  
Perceptual Quality ◽  
Camera Motion ◽  
End User ◽  
Content Type ◽  
Factors Affecting ◽  
Subjective Experiment

360-degree Virtual Reality (VR) videos have already taken up viewers’ attention by storm. Despite the immense attractiveness and hype, VR conveys a loathsome side effect called “cybersickness” that often creates significant discomfort to the viewers. It is of great importance to evaluate the factors that induce cybersickness symptoms and its deterioration on the end user’s Quality-of-Experience (QoE) when visualizing 360-degree videos in VR. This manuscript’s intent is to subjectively investigate factors of high priority that affect a user’s QoE in terms of perceptual quality, presence, and cybersickness. The content type (fast, medium, and slow), the effect of camera motion (fixed, horizontal, and vertical), and the number of moving targets (none, single, and multiple) in a video can be the factors that may affect the QoE. The significant effect of such factors on end-user QoE under various stalling events (none, single, and multiple) is evaluated in a subjective experiment. The results from subjective experiments show a notable impact of these factors on end-user QoE. Finally, to label the viewing safety concern in VR, we propose a neural network-based QoE prediction method that can predict the degree of cybersickness influenced by 360-degree videos under various stalling events in VR. The performance accuracy of the proposed method is then compared against well-known Machine Learning (ML) algorithms and existing QoE prediction models. The proposed method achieved a 90% prediction accuracy rate and performed well against existing models and other ML methods.

The analysis of the energy consumption of Chinese manufacturing based on the combination forecasting model

2015 ◽  
Vol 5 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Tianxiang Yao ◽  
Wenrong Cheng
Keyword(s):  
Energy Consumption ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Single Model ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Combination Forecasting ◽  
Combination Forecasting Model

Purpose – The purpose of this paper is to find a method that has high precision to forecast the energy consumption of China’s manufacturing industry. The authors hope the predicted data can provide references to the formulation of government’s energy strategy and the sustained growth of economy in China. Design/methodology/approach – First, the authors respectively make use of regression prediction model and grey system theory GM(1,1) model to construct single model based the data of 2001-2010, analyze the advantages and disadvantages of single prediction models. The authors use the data of 2011 and 2012 to test the model. Second, the authors propose combination forecasting model of manufacturing’s energy consumption in China by using standard variance to allocate the weight. Finally, this model is applied to forecast China’s manufacturing energy consumption during 2013-2016. Findings – The result shows that the combination model is a better one with higher accuracy; the authors can take the model as an effective tool to predict manufacturing’s energy consumption in China. And the energy consumption of China’s manufacturing industry continued to show a steady incremental trend. Originality/value – This method takes full advantages of the effective information reflected by the single model and improves the prediction accuracy.

Fitting and forecasting of nonlinear Taylor rule in China

2015 ◽  
Vol 5 (4) ◽  
pp. 402-420 ◽  
Author(s):  
Guobing Wu ◽  
Hao Zhang ◽  
Ping Chen
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Taylor Rule ◽  
Prediction Method ◽  
Monetary Policies ◽  
Content Type ◽  
Taylor Rules ◽  
Non Linear

Purpose – In this paper, six forms of non-linear Taylor rule have been applied to compare the fitting and prediction of response function of monetary policy of China, in an attempt to figure out a form of non-linear Taylor rule that accords with Chinese practices. The paper aims to discuss this issue. Design/methodology/approach – In this paper, the authors will conduct in-sample fitting and out-of-sample prediction on the response function of monetary policy of China by introducing the factor of exchange rate and by applying forward-looking, backward-looking and within-quarters non-linear Taylor rule with data from the first quarter of 1994 to the second quarter of 2011, with a view to provide reference for formulation and implementation of monetary policies of China. Findings – By analyzing the experimental data, the authors find that first, after introducing the factor of exchange rate, both the implementation effect and prediction ability of the monetary policies improve. Exchange rate has a relatively greater influence on the effect of the monetary policies during low inflation period. Introduction of exchange rate can improve the prediction accuracy of our monetary policies significantly. Second, as the implementation effect of monetary policy under different macro-background varies greatly, the situation should be correctly appraised when formulating and implementing monetary policies. According to the empirical results, the monetary policies have obvious non-linear characteristics, and transit smoothly with the change of inflation rate. On the two sides of inflation rate of 2.174 percent, there is an asymmetry response. Research limitations/implications – Surely, the conclusions are reached on the basis of quarterly data and one-step prediction method. It is no doubt that use of frequency mixing data including quarterly and monthly data will provide more sample information for studying relevant issues. And the use of multiple-step prediction method may cause a dynamic change of prediction indicators of models, which will help choose more appropriate prediction models. That is what the authors will study next. Originality/value – First, by introducing exchange rate, this paper will extend non-linear Taylor rules and test its applicability and fitting effect in China. Second, figure out a non-linear Taylor rule that conforms to Chinese practices with data. In this paper, multiple forms of non-linear Taylor rules and actual macro date will be adopted for fitting and finding out a non-linear Taylor rule that fits Chinese practices. Third, empirical basis will be provided for further perfecting monetary policies prediction models. As there are few studies in connection with the prediction accuracy of non-linear Taylor rules so far, this paper will compare and study the prediction accuracy of non-linear Taylor rules by utilizing multiple advanced prediction techniques, so as to offer a beneficial thinking for predicting and formulating monetary policies by the central bank.

On the fit and forecasting performance of grey prediction models for projecting educational attainment

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1387-1405 ◽  
Author(s):  
Hui-Wen Vivian Tang ◽  
Tzu-chin Rojoice Chou
Keyword(s):  
Educational Attainment ◽  
Predictive Power ◽  
Prediction Models ◽  
Exponential Smoothing ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Content Type ◽  
Equal Variance ◽  
Forecasting Performance ◽  
Out Of Sample

Purpose The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined with multiple linear regression employed by the National Center for Education Statistics (NCES). Design/methodology/approach An out-of-sample forecasting experiment was carried out to compare the forecasting performances on educational attainments among GM(1,1), GM(1,1) rolling, FGM(1,1) derived from the grey system theory and exponential smoothing prediction combined with multivariate regression. The predictive power of each model was measured based on MAD, MAPE, RMSE and simple F-test of equal variance. Findings The forecasting efficiency evaluated by MAD, MAPE, RMSE and simple F-test of equal variance revealed that the GM(1,1) rolling model displays promise for use in forecasting educational attainment. Research limitations/implications Since the possible inadequacy of MAD, MAPE, RMSE and F-type test of equal variance was documented in the literature, further large-scale forecasting comparison studies may be done to test the prediction powers of grey prediction and its competing out-of-sample forecasts by other alternative measures of accuracy. Practical implications The findings of this study would be useful for NCES and professional forecasters who are expected to provide government authorities and education policy makers with accurate information for planning future policy directions and optimizing decision-making. Originality/value As a continuing effort to evaluate the forecasting efficiency of grey prediction models, the present study provided accumulated evidence for the predictive power of grey prediction on short-term forecasts of educational statistics.

scholarly journals Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

2021 ◽  
Author(s):  
Aodi Sui ◽  
Wuyong Qian
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Prediction Method ◽  
Small Sample ◽  
Wind Power Generation ◽  
Collaborative Optimization ◽  
Grey Prediction ◽  
Model Based ◽  
Proposed Model ◽  
The Future

Renewable energy represented by wind energy plays an increasingly important role in China's national energy system. The accurate prediction of wind power generation is of great significance to China's energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China's wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China's energy-saving and emission reduction targets.

scholarly journals Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Liang Ye ◽  
Xintao Xia ◽  
Zhen Chang
Keyword(s):  
Time Series ◽  
Bootstrap Method ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Grey System Theory ◽  
Dynamic Monitoring ◽  
Grey System ◽  
Dynamic Prediction ◽  
The Future

A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails.

A novel grey fixed weight cluster model based on interval grey numbers

2017 ◽  
Vol 7 (2) ◽  
pp. 156-167 ◽  
Author(s):  
Jing Ye ◽  
Yaoguo Dang
Keyword(s):  
Weight Function ◽  
Cluster Model ◽  
Medical Science ◽  
Grey System Theory ◽  
Central Point ◽  
Content Type ◽  
Model Based ◽  
Clustering Model ◽  
Fixed Weight ◽  
Grey Clustering

Purpose Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers. Design/methodology/approach A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained. Findings The model is verified by a case which reveals a high distinguishability, validity and practicability. Practical implications This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on. Originality/value The central-point triangular whitenization weight function is introduced. The method reflects the thought “make full use of the information” in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.

MGM(1,m) model based on interval grey number sequence and its applications

2017 ◽  
Vol 7 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Pingping Xiong ◽  
Yue Zhang ◽  
Bo Zeng ◽  
Tian-Xiang Yao
Keyword(s):  
Prediction Models ◽  
Development Trend ◽  
Upper And Lower Bounds ◽  
Number Sequence ◽  
Content Type ◽  
Model Based ◽  
Grey Forecasting ◽  
Number Sequences ◽  
Grey Number ◽  
Predicted Values

Purpose Aiming at the traditional multivariate grey forecasting model only considers the modelling of real numbers; therefore, the purpose of this paper is to construct an MGM(1, m) model based on the interval grey number sequences according to the grey modelling theory. Design/methodology/approach First, the multivariable grey number sequences are transformed into the kernel and grey radius sequences which are two feature sequences of interval grey number sequences. Then the MGM(1, m) model for kernel sequences and grey radius sequences are established, respectively. Finally, the simulation and prediction of the upper and lower bounds of the interval grey number sequences are realized by the reductive calculation of the predicted values of the kernel and grey radius. Findings The model is applied to the prediction of visibility and relative humidity, the identification factors of the haze. The results show that the model has high accuracy on the simulation and prediction of multivariable grey number sequences, which is reasonable and practical. Originality/value The main contribution of this paper is to propose a method to simulate and forecast the multivariable grey number sequence that is to establish the prediction models for the whitening sequences of multivariable grey number sequences which are kernel and grey radius sequences and extend the possibility boundary of kernel by grey radius. The model can reflect the development trend of multivariable grey number sequence accurately. When the grey information is continuously complemented, the multivariable grey number prediction model is transformed into the traditional MGM(1, m) model. Therefore, the MGM(1, m) model based on interval grey number sequence is the generalisation and expansion of the traditional MGM(1, m) model.

scholarly journals Analysis and forecast of marine economy development in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yin Kedong ◽  
Zhe Liu ◽  
Caixia Zhang ◽  
Shan Huang ◽  
Junchao Li ◽  
...  
Keyword(s):  
Prediction Method ◽  
Current Situation ◽  
Policy Recommendations ◽  
Economic Activities ◽  
Growth Trend ◽  
Development Environment ◽  
Content Type ◽  
Marine Industry ◽  
The Future ◽  
Marine Economy

PurposeIn recent years, China's marine industry has maintained rapid growth in general, and marine-related economic activities have continued to improve. The purpose of this research is to analyze the basic situation of China's marine economy development, identify the problems therein, forecast development trends and propose policy recommendations accordingly.Design/methodology/approachThis research conducts a comprehensive and detailed analysis of the development of China's marine economy with rich data in diversified aspects. The current situation of China's marine economy development is analyzed from the perspective of scale and structure, and the external and internal development environment of China's marine economy is discussed. With the application of measurement and prediction method such as trend extrapolation, exponential smoothing, grey forecasting and neural network method, the future situation of China's marine economy development is forecasted.FindingsIn a complex environment where uncertainties at home and abroad have increased significantly, China's marine economy development suffers tremendous downward pressure in recent years. As China has achieved major achievements in the prevention and control of the COVID-19 epidemic, the marine economy development will gradually return to normal. It is estimated that the gross marine production value in 2022 will exceed 10 trillion yuan. China's marine economy will continue to maintain a steady growth trend in the future, and its development prospects will remain promising.Originality/valueThis research explores the current situation and trends of China's marine economy development and puts forward policy recommendations to promote the steady and health development of China's marine economy accordingly.

Development prediction of logistics industry in Henan province and its dynamic analysis

2014 ◽  
Vol 4 (2) ◽  
pp. 186-194 ◽  
Author(s):  
Yimin Huang
Keyword(s):  
Future Development ◽  
Regional Economy ◽  
Prediction Models ◽  
Development Trend ◽  
Henan Province ◽  
Grey Prediction ◽  
Logistics Industry ◽  
Content Type ◽  
The Future ◽  
Freight Volume

Purpose – The purpose of this paper is to establish a group of grey prediction models and relative degree model to study the characteristics and trend of the logistics industry development in Henan province scientifically. The study results can provide references for the development policy of the logistics industry in Henan province. Design/methodology/approach – The paper constructs grey prediction models and grey buffer operator models which are related to the distribution of logistics industry in Henan province, and selects prediction models by comparing model accuracy, and use them to forecast the development trend of logistics industry in future ten years of Henan province. Using the grey relative models, the paper analyses development dynamic and prospect which support the development of logistics industry, and provide some references for transferring the pattern of economic growth of Henan province, forming new economic growth point and formulating relevant policies. High prediction accuracy models are selected to forecast the future development trend of logistics industry in the next ten years. Findings – Results show that the modern logistics industry in Henan province has been a steady growth in overall, the main growth points of the logistics industry development in Henan province are roadway miles (km), roadway (100 million tonnes/km), freight turnover (100 million tonnes/km) and waterway (100 million tonnes), the growth points for the future development of logistics industry in Henan province are the roadway freight volume, roadway passenger volume and waterway freight volume. Practical implications – Regional economic competition has become an important index for measuring a country's economic development level. Logistics industry plays an important role in the regional economic development, such as promoting coordinated development of regional economy and upgrading industrial optimization, and playing a major role in industrial transfer. Hence, logistics industry, which is urgently needed to solve by the government, has become important forces for promoting the growth of economy and a basic pillar industries of regional economy. Originality/value – The paper presents the systematic results of development prediction of modern logistics industry in Henan province and its dynamic analysis by using grey systems theory, not only to predict the trend of the development of the logistics industry, also to analyse the future development of logistics industry in the leading power factors.

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