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
Authors | Cui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun |
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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. |
Keywords | short time series; rolling mechanism; grey model; meta-heuristic optimization algorithms; forecasting |
Year | 2021 |
Journal | Neural Computing and Applications |
Journal citation | 33 (17), pp. 11339-11353 |
Publisher | Springer |
ISSN | 0941-0643 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-020-05658-0 |
Scopus EID | 2-s2.0-85099474468 |
Page range | 11339-11353 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 15 Jan 2021 |
Publication process dates | |
Accepted | 22 Dec 2020 |
Deposited | 06 Jul 2023 |
https://acuresearchbank.acu.edu.au/item/8z3y3/a-hybrid-rolling-grey-framework-for-short-time-series-modelling
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