An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

Liu, Li; Liu, Ming; Li, Lian

doi:10.1155/2014/641514

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…The rolling mechanism is an effective method to improve the performance of GM that updates the input data by discarding old data for each loop in grey prediction. The purpose is that, in each rolling step, the data utilized for the next forecast is the most recent data calculated from the assembled model [21]. For example, if we take the period from 2010 to 2015 as an input time series, then we calculate the first model from values x (0) (2010), x (0) (2011), ..., x (0) (2015).…”

Section: Rolling Mechanismmentioning

confidence: 99%

Forecasting greenhouse gas emissions of the Slovak Republic based on grey models

Vojteková¹,

Blažeková²,

Pavličko³

2022

Environ. Protect. Eng.

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Reducing greenhouse gas (GHG) emissions has become a necessity and not an option to sustain the environment in both human and natural systems. The Slovak Republic (SR), like the European Union (EU), aims to become greenhouse gas neutral by 2050. To reach this ambitious target, emissions will need to fall by 55% compared to those in the base year 1990. Therefore, forecasting GHG emission amounts is important. The grey model is one of the widespread mathematical forecasting methods. There exist studies that have used some types of grey models to predict GHG but not in the case of the Slovak Republic. We have optimized the length of the input sequence in the rolling mechanism to enhance the forecast accuracy of a new grey model combining the Bernoulli equation with the rolling mechanism. Standard grey model, nonlinear grey Bernoulli model, and grey model with rolling mechanism were used to prove the validity of our optimization and to compare prediction performance among grey models. The novel model was also used for a long-term forecast of GHG emissions in the SR for the years from 2020 to 2040 and compared with officially reported projections. Calculated values showed that the SR is on a good way to reach set targets towards climate change mitigation.

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Section: Rolling Mechanismmentioning

confidence: 99%

Forecasting greenhouse gas emissions of the Slovak Republic based on grey models

Vojteková¹,

Blažeková²,

Pavličko³

2022

Environ. Protect. Eng.

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“…intervals, probability distributions, fuzzy sets, and imprecise probability distributions) are difficult for probability and fuzzy mathematics to handle (Li et al, 2007;Shen et al, 2013). The Grey Model (GM) is based on the grey system theory and has been widely applied in economics and financial areas (Kayacan, Ulutas, & Kaynak, 2010;Li et al, 2007;L. Liu, Wang, Liu, & Li, 2014), public health and biostatistics (Feng & Zhang, 2012;Jin et al, 2008;Shen et al, 2013), engineering (Kang & Zhao, 2012) as well as other specialties.…”

Section: Forecasting Models and Data Analysismentioning

confidence: 99%

Immigration trends’ impacts on state vocational rehabilitation agency minority application rates: An empirical forecast model demonstration study

Moore

Wang²,

Eugene-Cross³

et al. 2016

JVR

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“…Brain Storm Optimization Algorithm. Parameter optimization is one of the effective ways to improve the accuracy of forecasting approaches [16][17][18][19]. In this paper, the brain storm optimization (BSO) algorithm [20] is used to optimize the unknown parameters in the GNN model.…”

Section: Abstract and Applied Analysismentioning

confidence: 99%

A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting

Sun

2014

Abstract and Applied Analysis

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Stock index forecasting is an important tool for both the investors and the government organizations. However, due to the inherent large volatility, high noise, and nonlinearity of the stock index, stock index forecasting has been a challenging task for a long time. This paper aims to develop a novel hybrid stock index forecasting model named BSO-GNN based on the brain storm optimization (BSO) approach and the grey neural network (GNN) model by taking full advantage of the grey model in dealing with data with small samples and the neural network in handling nonlinear fitting problems. Moreover, the new developed BSO-GNN, which initializes the parameters in grey neural network with the BSO algorithm, has great capability in overcoming the deficiencies of the traditional GNN model with randomly initialized parameters through solving the local optimum and low forecasting accuracy problems. The performance of the proposed BSO-GNN model is evaluated under the normalization and nonnormalization preprocessing situations. Experimental results from the Shanghai Stock Exchange (SSE) Composite Index, the Shenzhen Composite Index, and the HuShen 300 Index opening price forecasting show that the proposed BSO-GNN model is effective and robust in the stock index forecasting and superior to the individual GNN model.

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An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

Cited by 9 publications

References 31 publications

Forecasting greenhouse gas emissions of the Slovak Republic based on grey models

Forecasting greenhouse gas emissions of the Slovak Republic based on grey models

Immigration trends’ impacts on state vocational rehabilitation agency minority application rates: An empirical forecast model demonstration study

A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting

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