An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK

•A more reliable method for estimating the parameters of GM(n,m) models.•A diagnostic for deciding whether a chosen GM(n,m) is data compatible.•A generalisation of the Grey–Verhulst offering greater flexibility for data fitting.•Derivation of short to medium term predictions of steel intensity of us...

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Vydáno v:Expert systems with applications Ročník 41; číslo 4; s. 1236 - 1244
Hlavní autor: Evans, Mark
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Ltd 01.03.2014
Elsevier
Témata:
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Economics
Estimating
Estimation
Exact sciences and technology
Expert systems
Fuzzy logic
General aspects
GM(1,1)
Grey models
Inference from stochastic processes; time series analysis
Intensity of use
Iron and steel making
Mathematical models
Mathematics
Metals. Metallurgy
Model verification
Neural networks
Prediction
Probability and statistics
Production of metals
Sciences and techniques of general use
Statistics
Steels
Time series
Grey models
Model verification
Intensity of use
Estimation
Prediction
GM(1,1)
Parameter estimation
Grey system
Evolutionary algorithm
Time series
Econometric model
Systems theory
Use study
Neural network
Modeling
Forecasting
Fuzzy neural nets
Genetic algorithm
Statistical model
Iron steel industry
Causality
ISSN:0957-4174, 1873-6793
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Shrnutí:•A more reliable method for estimating the parameters of GM(n,m) models.•A diagnostic for deciding whether a chosen GM(n,m) is data compatible.•A generalisation of the Grey–Verhulst offering greater flexibility for data fitting.•Derivation of short to medium term predictions of steel intensity of use in the UK. Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.08.006