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

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

Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular, and nonstationary. The size of these economic datasets is often very small. Many models based on grey system theory could be adapted to various economic time series data. However, some o...

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Main Authors: Li Liu, Qianru Wang, Ming Liu, Lian Li
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/641514
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spelling doaj-88add72c9500412599b229e262baa36f2020-11-24T22:33:40ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/641514641514An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic DatasetLi Liu0Qianru Wang1Ming Liu2Lian Li3School of Computing, National University of Singapore, 117417, SingaporeSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaFaculty of Computer and Information Science, Southwest University, Chongqing 400715, ChinaDepartment of Computer Science and Technology, HeFei University of Technology, Hefei 230009, ChinaGrey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular, and nonstationary. The size of these economic datasets is often very small. Many models based on grey system theory could be adapted to various economic time series data. However, some of these models did not consider the impact of recent data or the effective model parameters that can improve forecast accuracy. In this paper, we proposed the PRGM(1,1) model, a rolling mechanism based grey model optimized by the particle swarm optimization, in order to improve the forecast accuracy. The experiment shows that PRGM(1,1) gets much better forecast accuracy among other widely used grey models on three actual economic datasets.http://dx.doi.org/10.1155/2014/641514
collection DOAJ
language English
format Article
sources DOAJ
author Li Liu
Qianru Wang
Ming Liu
Lian Li
spellingShingle Li Liu
Qianru Wang
Ming Liu
Lian Li
An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
Abstract and Applied Analysis
author_facet Li Liu
Qianru Wang
Ming Liu
Lian Li
author_sort Li Liu
title An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
title_short An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
title_full An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
title_fullStr An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
title_full_unstemmed An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset
title_sort intelligence optimized rolling grey forecasting model fitting to small economic dataset
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2014-01-01
description Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular, and nonstationary. The size of these economic datasets is often very small. Many models based on grey system theory could be adapted to various economic time series data. However, some of these models did not consider the impact of recent data or the effective model parameters that can improve forecast accuracy. In this paper, we proposed the PRGM(1,1) model, a rolling mechanism based grey model optimized by the particle swarm optimization, in order to improve the forecast accuracy. The experiment shows that PRGM(1,1) gets much better forecast accuracy among other widely used grey models on three actual economic datasets.
url http://dx.doi.org/10.1155/2014/641514
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