High-dimensional lag structure optimization of fuzzy time series

Ruobin Gao*, Okan Duru, Kum Fai Yuen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Lag-selection is a high dimensional hyper-parameter in the fuzzy time series (FTS) which requires complex optimization process and computational capacity particularly in high frequency dataset (e.g. daily, hourly). Multivariate high order FTS suffers from establishing long logical relationships, and the difficulty of rule matching is proportional to the time lags and number of variables. In the vast majority of FTS literature, a grid search algorithm or evolutionary algorithms are run to find singular time-lags. In addition, some researchers determine the lag structure arbitrarily. However, grid search in high dimensional problems is not practical especially when recursive predictions are generated and evolutionary algorithms suffer from the randomness which may generate different solutions. This paper proposes an alternative approach to the lag selection problem by utilizing supervised principal component analysis (SPCA), and the lag structure is reduced to low dimensional space. SPCA has been developed to project the high dimensional lagged variables into the first principal component. An empirical study is conducted to validate the proposed approach by using global shipping industry data in the world.

Original languageEnglish
Article number114698
JournalExpert Systems with Applications
Volume173
DOIs
Publication statusPublished - Jul 1 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

ASJC Scopus Subject Areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

Keywords

  • Fuzzy time series
  • Lagged variable selection
  • Predictive analytic
  • Supervised PCA

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