A HMM and grey model based ERL forecasting method

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

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Research of combined grey model based on entropy weight for predicting anchor bolt bearing capacity

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/660/1/012080 ◽

2021 ◽

Vol 660 (1) ◽

pp. 012080

Author(s):

Xiaoyun Sun ◽

Hui Xing ◽

Junyao Zhang

Keyword(s):

Bearing Capacity ◽

Grey Model ◽

Entropy Weight ◽

Anchor Bolt ◽

Model Based

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

Australian Journal of Business and Management Research ◽

10.52283/nswrca.ajbmr.20110107a01 ◽

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.

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Trust and grey model based data aggregation algorithm

International Journal of Sensor Networks ◽

10.1504/ijsnet.2021.10037355 ◽

2021 ◽

Vol 35 (4) ◽

pp. 258

Author(s):

Ni Wang ◽

Li Li ◽

Yansui Du ◽

Jun Wang

Keyword(s):

Data Aggregation ◽

Grey Model ◽

Model Based

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Financial Time Series Forecasting Model Based on EMD and Rolling Grey Model

2020 IEEE Workshop on Signal Processing Systems (SiPS) ◽

10.1109/sips50750.2020.9195218 ◽

2020 ◽

Author(s):

Binjing Li ◽

Fenggui Liu ◽

Jun Lin ◽

Zhongfeng Wang

Keyword(s):

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

Grey Model ◽

Forecasting Model ◽

Financial Time ◽

Model Based

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A novel grey model based on traditional Richards model and its application in COVID-19

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2020.110480 ◽

2020 ◽

pp. 110480

Author(s):

Xilin Luo ◽

Huiming Duan ◽

Kai Xu

Keyword(s):

Grey Model ◽

Richards Model ◽

Model Based

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The housing demand analysis and prediction of the real estate based on the AWGM (1, N) model

Grey Systems Theory and Application ◽

10.1108/gs-09-2019-0035 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xin Xiong ◽

Huan Guo ◽

Xi Hu

Keyword(s):

Real Estate ◽

Housing Market ◽

Housing Demand ◽

Grey Model ◽

Content Type ◽

First Order ◽

Model Based ◽

Per Capita ◽

Improved Model ◽

Grey Relational

PurposeThe purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.Design/methodology/approachThis paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.FindingsThis paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.Practical implicationsThe model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.Originality/valueThe factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

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