A stratified model for short-term prediction of time series

Yihao Zhang*, Mehmet A. Orgun, Rohan Baxter, Weiqiang Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

This paper develops a model for short-term prediction of time series based on Element Oriented Analysis (EOA). The EOA model represents nonlinear changes in a time series as strata and uses these in developing a predictive model. The strata features used by the EOA model have the potential to improve its forecasting performance on non-linear data relative to the performance of existing methods. We demonstrate the characteristics of the EOA model using an empirical study of stock indices from eight major stock markets. The study provides comparisons of the accuracy and time efficiency between ARIMA, Neural Networks and the EOA model. Our findings indicate that the EOA model is a promising approach for short-term time series prediction.

Original languageEnglish
Title of host publicationPRICAI 2010: Trends in Artificial Intelligence - 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsByoung-Tak Zhang, Mehmet A. Orgun
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages372-383
Number of pages12
Volume6230 LNAI
ISBN (Print)3642152457, 9783642152450
DOIs
Publication statusPublished - 2010
Event11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010 - Daegu, Korea, Republic of
Duration: 30 Aug 20102 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6230 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
CountryKorea, Republic of
CityDaegu
Period30/08/102/09/10

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