An enhanced deterministic fuzzy time series forecasting model

Sheng Tun Li, Yi Chung Cheng

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.

Original languageEnglish
Pages (from-to)211-235
Number of pages25
JournalCybernetics and Systems
Volume40
Issue number3
DOIs
Publication statusPublished - 2009 Apr 1

Fingerprint

Time series
Redundancy
Fuzzy logic
Costs

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Artificial Intelligence

Cite this

Li, Sheng Tun ; Cheng, Yi Chung. / An enhanced deterministic fuzzy time series forecasting model. In: Cybernetics and Systems. 2009 ; Vol. 40, No. 3. pp. 211-235.
@article{418f1c96a1934d328985610b9b17fba3,
title = "An enhanced deterministic fuzzy time series forecasting model",
abstract = "The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.",
author = "Li, {Sheng Tun} and Cheng, {Yi Chung}",
year = "2009",
month = "4",
day = "1",
doi = "10.1080/01969720802715128",
language = "English",
volume = "40",
pages = "211--235",
journal = "Cybernetics and Systems",
issn = "0196-9722",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

An enhanced deterministic fuzzy time series forecasting model. / Li, Sheng Tun; Cheng, Yi Chung.

In: Cybernetics and Systems, Vol. 40, No. 3, 01.04.2009, p. 211-235.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An enhanced deterministic fuzzy time series forecasting model

AU - Li, Sheng Tun

AU - Cheng, Yi Chung

PY - 2009/4/1

Y1 - 2009/4/1

N2 - The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.

AB - The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.

UR - http://www.scopus.com/inward/record.url?scp=61549109804&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=61549109804&partnerID=8YFLogxK

U2 - 10.1080/01969720802715128

DO - 10.1080/01969720802715128

M3 - Article

AN - SCOPUS:61549109804

VL - 40

SP - 211

EP - 235

JO - Cybernetics and Systems

JF - Cybernetics and Systems

SN - 0196-9722

IS - 3

ER -