A novel grey forecasting model and its optimization

Cui, Jie; Liu, Sifeng; Zeng, Bo; Xie, Naiming

doi:10.1016/j.apm.2012.09.052

Cited by 205 publications

(128 citation statements)

References 4 publications

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“…Because of the existence of differences between regions of low-rent housing in our country, too short time since the policy began, too difficult to obtain the data, these methods often had an unsatisfied performance when used to forecast. In recent years, the grey system theory has been applied in areas such as agriculture [1], meteorology [2], and mostly come up with satisfactory results. Therefore, we consider use grey related theory to forecast housing supply.…”

Section: A Gm(11) Modelmentioning

confidence: 99%

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Li¹

2013

2013 Sixth International Conference on Business Intelligence and Financial Engineering

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In this paper, we use grey GM (1, 1) model to predict low rental housings' supply quantity in Beijing from 2013 to 2015.Firstly, we give a brief introduction of renewal grey GM (1, 1) model and its methodology. Secondly, we use per capital floor space of urban residential buildings and nonagricultural population as basic data to predicts Beijing's total housing supply from 2014 to 2016. Finally, we use grey GM (1, 1) model predict the number and the area of low rent housings' supply target in Beijing

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Section: A Gm(11) Modelmentioning

confidence: 99%

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Li¹

2013

2013 Sixth International Conference on Business Intelligence and Financial Engineering

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“…Some researchers concentrate upon improvements of GM(1, 1) model mainly in the following aspects: grey derivative [9], background value [10], initial condition [9], time response function [11], parameter estimation [12]. And many hybrid models based on GM(1, 1) were proposed [1,13].…”

Section: Introductionmentioning

confidence: 99%

Properties of the GM(1,1) with fractional order accumulation

Liu

Fang

et al. 2015

Applied Mathematics and Computation

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“…Shih et al [8] improved the GM(1, 1) by changing the background and initial values. Cui et al [9] provided the NGM(1, 1) based on the GM(1, 1), which belongs to an entirely optimized GM(1, 1). Chen et al [10] proposed the MGM for predicting the varied capacity with discharge cycles in electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

A New Error Prediction Method for Machining Process Based on a Combined Model

Zhou

Zhu

Wang

et al. 2018

Mathematical Problems in Engineering

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Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. The advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions.

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A novel grey forecasting model and its optimization

Cited by 205 publications

References 4 publications

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Properties of the GM(1,1) with fractional order accumulation

A New Error Prediction Method for Machining Process Based on a Combined Model

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