Interval Prediction and Stability Analysis of Time Series (Part I: Theory)

DOI

10.2991/icsnce-16.2016.38How to use a DOI?

Keywords

Time series; Grey bootstrap method; Interval prediction; Fuzzy-set theory; Stability

Abstract

Time series of different performance attributes are produced in the process of runtime of products, and any time series contain a large amount of information about the system evolution, so the information of the future evolution can be extracted from the time series to make a forecast or stability analysis. In this paper, based on the grey bootstrap method to establish a grey bootstrap distribution of time data sequences, the interval prediction of performance signal can be obtained by the given confidence level; Then according to the fuzzy-set theory, the fuzzy similar relation of engineering practice is changed into the fuzzy equivalence relation of space vector, and the stability analysis of time series is acquired by the given threshold. Two sets of models can effectively assess the change trend and performance evolution signs of time series, helping us to timely grasp the work performance situation.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xintao Xia
AU  - Zhen Chang
AU  - Yunfei Li
AU  - Bin Liu
AU  - Liang Ye
PY  - 2016/07
DA  - 2016/07
TI  - Interval Prediction and Stability Analysis of Time Series (Part I: Theory)
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 192
EP  - 196
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.38
DO  - 10.2991/icsnce-16.2016.38
ID  - Xia2016/07
ER  -