GM(1,1) Model of Moving Average Operation to the Original Sequence and its Application

2011 ◽  
Vol 225-226 ◽  
pp. 381-384
Author(s):  
Bin Zeng ◽  
Wu Jun Zeng
Keyword(s):  
Data Processing ◽  
Integral Method ◽  
System Development ◽  
Moving Average ◽  
Original Data ◽  
Forecasting Model ◽  
Original Sequence ◽  
Background Values ◽  
Equal Interval ◽  
Grey Forecasting

The grey forecasting model has been successfully adopted in various fields and its accuracy is closely related with the original data. Improving the smoothness of the original sequence can increase the accuracy of GM(1,1) model and many researchers have done such work about the original sequence improvement. The paper adopts moving average operation on the original sequence and gets the new sequence with good smoothness. In the process of the establishment of GM(1,1) model the paper adopts the integral method to get the background values and expand the equal interval into the unequal interval. The examples show the method can fit and predict the system development more accurately which provides a new way for the data processing.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

The exponential grey forecasting model for CO2 emissions in Taiwan

2015 ◽  
Vol 5 (3) ◽  
pp. 354-366 ◽  
Author(s):  
Chen-Fang Tsai ◽  
Shin-Li Lu
Keyword(s):  
Moving Average ◽  
Grey Model ◽  
Model Optimization ◽  
Content Type ◽  
Background Values ◽  
Optimal Weights ◽  
Grey Forecasting ◽  
Grey Models ◽  
Forecast Models ◽  
Weighted Moving Average

Purpose – The purpose of this paper is to improve the forecasting efficiency of a grey model. Design/methodology/approach – The exponentially weighted moving average (EWMA) algorithm is proposed to modify background values for a new grey model optimization. Findings – The experimental results reveal that the proposed models (EGM, REGM) outperform traditional grey models. Originality/value – A genetic algorithm (GA) optimizer is used to select the optimal weights for the background values of the EGM(1,1) and REGM(1,1) forecast models. The results of the current study are very encouraging, as the empirical results show that the REGM(1,1) and EGM(1,1) models reduce the MAPE rates over the traditional GM(1,1) and RGM(1,1) models.

scholarly journals Permodelan Produksi Kopi Indonesia dengan Menggunakan Seasonal Autoregressive Integrated Moving Average (SARIMA)

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Hutomo Atman Maulana ◽  
Kasuma Wardany Harahap ◽  
Adriyansyah Adriyansyah ◽  
Rofiroh Rofiroh ◽  
Fuad Zainuddin
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Production Data ◽  
Coffee Production ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Data

This research used a method in modelling time series data in the form of seasonal data. The method used in this study is the Seasonal Autoregressive Integrated Moving Average (SARIMA). This method is applied to Indonesian coffee production data from January 2009 - December 2013 with the aim of obtaining a model that will be used to predict the amount of coffee production in January 2014 - December 2014. The forecasting results from the next model will be compared with the original data. Data processing is done using EViews software. Based on the results of data processing, the best model for forecasting is obtained, SARIMA (2,1,0) (1,1,1)12

Unequal Interval GM(1,1) Model of Weighted Coefficient Adjustment and its Application

2011 ◽  
Vol 219-220 ◽  
pp. 428-431 ◽  
Author(s):  
Bin Zeng ◽  
Lin Fang Li
Keyword(s):  
Fatigue Strength ◽  
System Application ◽  
Original Data ◽  
Forecasting Model ◽  
Time Interval ◽  
Difference Quotient ◽  
Grey System ◽  
Strength Data ◽  
Grey Forecasting ◽  
The Difference

Although the grey forecasting model has been successfully adopted in various fields and has been demonstrated prospected aspect. The paper makes the original data multiply the time interval to get the accumulated sequence and uses the difference quotient method to establish the unequal interval GM(1,1) model. In order to reduce the error the paper adopts the weighted coefficient to revise the background values. By fitting and predicting the fatigue strength data it proves the method in the paper is effective which improves the accuracy of the model. It provides an effective way for the grey system application.

Pemanfaatan Sistem Informasi Terhadap Kinerja Pelayanan, Sirkulasi Buku Dan Pengolahan Arsip

2018 ◽  
Vol 4 (1) ◽  
pp. 87-96
Author(s):  
Yanni Suherman
Keyword(s):  
Information System ◽  
Data Processing ◽  
System Development ◽  
Processing System ◽  
Data Processing System ◽  
Data Archiving ◽  
Data Archives ◽  
Development Life Cycle ◽  
Search Data

Research conducted at the Office of Archives and Library of Padang Pariaman Regency aims to find out the data processing system library and data archiving. All data processing is done is still very manual by using the document in writing and there is also a stacking of archives on the service. By utilizing library information systems and archives that will be applied to the Office of Archives and Library of Padang Pariaman Regency can improve the quality of service that has not been optimal. This research was made by using System Development Life Cycle (SDLC) which is better known as waterfall method. The first step taken on this method is to go directly to the field by conducting interviews and discussions. This information system will be able to assist the work of officers in terms of data processing libraries and facilitate in search data archives by presenting reports more accurate, effective and efficient.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

Grey prediction models for the standard limit of vehicle noise

2017 ◽  
Vol 232 (7) ◽  
pp. 973-979 ◽  
Author(s):  
Zhendong Zhao ◽  
Changzheng Hu
Keyword(s):  
Prediction Models ◽  
System Development ◽  
Original Data ◽  
Grey Theory ◽  
Grey Prediction ◽  
Vehicle Noise ◽  
Direct Model ◽  
The Future ◽  
Quadratic Fitting ◽  
Verhulst Model

With an increasing number of vehicles and increasing environmental protection requirements, countries have accelerated the rate of revision of automobile noise standards and legislation. Scientific prediction of the limiting values in future noise standards is helpful to promote the development of automobile noise reduction technology and measurement analysis technology. The development of noise standard limits has its own objective laws and is restricted to the current and future developments in automotive technology. The amplitude of noise will be reduced increasingly less in the future. Grey prediction theory can explore the variation rules by processing a few effective data. In this paper, grey theory is used to deal with the limited original data in the vehicle noise standard. Non-equal-interval quadratic fitting of the grey Verhulst direct model to predict the future noise standard limits is selected on the basis of calculation and comparison of different models. The Verhulst model is employed to describe the system development by using the characteristics of saturation. By means of quadratic fitting, the accuracy of the Verhulst model can be further improved. The simulation results show the validity and the accuracy of the model. The prediction result is useful for standards and regulations makers and for car manufacturers.

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

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