Data-based structure selection for unified discrete grey prediction model

Date

2019-06-24

Advisors

Journal Title

Journal ISSN

ISSN

0957-4174

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Grey models have been reported to be promising for time series prediction with small samples, but the diversity kinds of model structures and modelling assumptions restrains their further applications and developments. In this paper, a novel grey prediction model, named discrete grey polynomial model, is proposed to unify a family of univariate discrete grey models. The proposed model has the capacity to represent most popular homogeneous and non-homogeneous discrete grey models and furthermore, it can induce some other novel models, thereby highlighting the relationship between the models and their structures and assumptions. Based on the proposed model, a data-based algorithm is put forward to select the model structure adaptively. It reduces the requirement for modeler’s knowledge from an expert system perspective. Two numerical experiments with large-scale simulations are conducted and the results show its effectiveness. In the end, two real case tests show that the proposed model benefits from its adaptive structure and produces reliable multi-step ahead predictions.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Grey system theory, Discrete grey model, Structure selection, Matrix decomposition

Citation

Song, B., Xie, N. and Yang, Y. (2019) Data-based structure selection for unified discrete grey prediction model, Expert Systems with Applications, 136, pp.264-275.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)