Forecasting the Global Photovoltaic Market by Using the GM(1,1) Grey Forecasting Method

2011 ◽  
Author(s):  
Chi-Yo Huang ◽  
Wei-Chang Tzeng ◽  
Yu-Wei Liu ◽  
Po-Yen Wang
Keyword(s):  
Grey Forecasting ◽  
Forecasting Method

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

Application of Self-Adaptive MGM (1, n) Model in Dam Deformation Prediction

2013 ◽  
Vol 740 ◽  
pp. 284-288
Author(s):  
He Zhi Liu ◽  
Song Lin Wang ◽  
Jing Yang Liu ◽  
Guang Yang
Keyword(s):  
Estimation Method ◽  
Original Data ◽  
Arch Dam ◽  
Deformation Prediction ◽  
Grey Forecasting ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Dam Deformation ◽  
Data Group ◽  
Self Adaptive

Considering the characteristics of randomness and uncertainty of dam system and the lack of safety monitoring data in some projects, a grey forecasting method based on self-adaptive MGM (1, n) was proposed in this paper to predict the dam deformation. Firstly, theory of the traditional MGM (1, n) model and the parameter estimation method were introduced. On the basis of this, add these forecasted values into the original data group and eliminate the oldest information, the self-adaptive MGM (1, n) model could be established. This paper employs this improved approach in the dam deformation of an arch dam. By predicting the dam deformation in next 5 days, the validity of such method was proved. Compared with GM (1, 1) model and conventional MGM (1, n) model, the experimental results indicate that the forecasting performance is significantly superior to that of the above mentioned two methods.

scholarly journals Aplikasi Metode Grey Forecasting Pada Peramalan Kebutuhan Bahan Bakar Alternatif Ramah Lingkungan di PT. Indocement Tunggal Prakarsa Tbk

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanda Lokita Nariswari ◽  
Cucuk Nur Rosyidi
Keyword(s):  
Time Series ◽  
Alternative Energy ◽  
Historical Data ◽  
Raw Materials ◽  
Time Series Forecasting ◽  
Short Term ◽  
Grey Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
A Company

<span><em>Forecasting is one of the methods required by a company to plan the demand of raw materials in the </em><span><em>future, in order to avoid the emergence of various problems such as stock out. However, not all </em><span><em>forecasting methods can be used to forecast demand in the short term a specially a condition where the </em><span><em>company only has a few historical data. Grey method is a forecasting method which can be used to </em><span><em>predict the short-term demand. The purpose of this study is to determine how well the Grey method used </em><span><em>to predict the demand of alternative energy and compared with other forecasting methods. Mean Squared </em><span><em>Error (MSE) is used as a measure of the goodness of the method. The result of the study indicates that the </em><span><em>Grey Forecasting Methods MSE value that is smaller than other time series forecasting methods.</em></span></span></span></span></span></span></span><br /></span>

The Methods of Corrosion Prediction for Materials in Natural Environment and their Applications

2014 ◽  
Vol 556-562 ◽  
pp. 51-56
Author(s):  
Ai Ai Zhang ◽  
Wei Fang Zhang ◽  
Yu Long Zhang ◽  
Tian Jiao Liu
Keyword(s):  
Neural Network ◽  
Natural Environment ◽  
Prediction Method ◽  
Environmental Damage ◽  
Linear Extrapolation ◽  
Function Model ◽  
Grey Forecasting ◽  
Forecasting Method ◽  
Neural Network Prediction ◽  
Network Prediction

It is of great significance for corrosion prediction to avoid sudden accident, reduce casualties and environmental damage. This paper introduces the basic methods of corrosion prediction for the materials in natural environment. Firstly, statistical methods of corrosion prediction, including the linear extrapolation prediction method based on the function model and the extrapolation prediction method based on the related model, are showed. Then, some modern and mathematic methods for the corrosion prediction, such as grey forecasting method, prediction method of neural network, prediction method of pattern recognition, have been expounded. Finally, the typical applications in natural environment of those methods have been summarized.

The Forecast of Transportation Flows under Certain and Uncertain Trends: The Application of Combined Grey System Models

2013 ◽  
Vol 409-410 ◽  
pp. 1098-1101
Author(s):  
Jian Hu Zheng
Keyword(s):  
Forecast Accuracy ◽  
Grey System ◽  
Short Term ◽  
System Models ◽  
Grey Forecasting ◽  
Changing Trends ◽  
Forecasting Method ◽  
Modern Economic ◽  
Correlated Variable ◽  
Transportation Flows

The complexity of modern economic system makes the forecast about correlated variable more difficult. Combined grey system models with general grey forecasting method and grey verhulst forecasting method are utilized to overcome the forecast issues under certain and uncertain changing trends. The results indicate that the forecast accuracy is high and short-term forecasts are available, which supports the methodological applicability and robustness.

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