Time delay analysis model based on the generalized dynamic grey incidence window concept

LI Chong, QU Yuling, QIAN Guanwen

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (12) : 3248-3261.

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中国科学院数学与系统科学研究院期刊网
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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (12) : 3248-3261. DOI: 10.12011/1000-6788-2018-2081-14

Time delay analysis model based on the generalized dynamic grey incidence window concept

  • LI Chong1, QU Yuling2, QIAN Guanwen1
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Abstract

Based on the criterion of the static maximum grey incidence degree, the classic time-delay analysis method strongly depends on time series samples. This affects the representativeness of the delay values, and even causes contradictory results. To solve these problems, this study develops a new dynamic time-delay analysis method. First, a new generalized grey incidence model is constructed by introducing a novel new information priority weighting operator. Then, to overcome the disadvantages of the widely used classic grey incidence-based time delay analysis method, a new concept of dynamic grey incidence window is proposed, which helps to extract all possible time delays between sequences. Based on this new model and the dynamic grey incidence window concept, the time-delay incidence matrix and related time-delay incidence vectors are obtained. A novel grey stack matrix is designed to realize the effective extraction of the representative value from all potential time delays between sequences. Finally, a case study of the time delays between some important macroeconomic indicator sequences is carried out. Compared with the classic method, result shows that the proposed time delay analysis method can provide more reliable representative time delay values.

Key words

grey incidence degree / time delay analysis / new information priority weighting operator

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LI Chong , QU Yuling , QIAN Guanwen. Time delay analysis model based on the generalized dynamic grey incidence window concept. Systems Engineering - Theory & Practice, 2019, 39(12): 3248-3261 https://doi.org/10.12011/1000-6788-2018-2081-14

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Funding

National Natural Science Foundation of China (71401039); Fujian Natural Science Foundation (2017J01517)
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