A hybrid rolling grey framework for short time series modelling

Journal article

Cui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun. (2021). A hybrid rolling grey framework for short time series modelling. Neural Computing and Applications. 33(17), pp. 11339-11353. https://doi.org/10.1007/s00521-020-05658-0
AuthorsCui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun
Abstract

Time series modelling is gaining spectacular popularity in the prediction process of decision making, with applications including real-world management and engineering. However, for short time series, prediction has to face unavoidable limitation for modelling extremely complex systems. It has to apply inadequate and incomplete data from short time to predict unknown observations. With such limited data source, existing techniques, such as statistical modelling or machine learning methods, fail to predict short time series effectively. To address this problem, this paper provides a global framework for short time series modelling predictions, integrating the rolling mechanism, grey model, and meta-heuristic optimization algorithms. In addition, dragonfly algorithm and whale optimization algorithm are investigated and deployed to optimize the framework by enhancing its predicting accuracy with less computational costs. To verify the performance of the proposed framework, three industrial cases are introduced as simulation experiments in this paper. Experimental results confirm that the framework solves corresponding short time series modelling predictions with greater accuracy and speed than the standard GM(1,1) models. The source codes of this framework are available at: https://github.com/zhesencui/HybridRollingGreyFramework.git.

Keywordsshort time series; rolling mechanism; grey model; meta-heuristic optimization algorithms; forecasting
Year2021
JournalNeural Computing and Applications
Journal citation33 (17), pp. 11339-11353
PublisherSpringer
ISSN0941-0643
Digital Object Identifier (DOI)https://doi.org/10.1007/s00521-020-05658-0
Scopus EID2-s2.0-85099474468
Page range11339-11353
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online15 Jan 2021
Publication process dates
Accepted22 Dec 2020
Deposited06 Jul 2023
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