A stratified model for short-term prediction of time series

Authors:
Yihao Zhang;Mehmet A. Orgun;Rohan Baxter;Weiqiang Lin
Affiliations:
Department of Computing, Macquarie University, Sydney, NSW, Australia;Department of Computing, Macquarie University, Sydney, NSW, Australia;Australian Taxation Office, Canberra, ACT, Australia;Australian Taxation Office, Canberra, ACT, Australia
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

Citing 4
Cited 0

Multiple neural networks for a long term time series forecast

Neural Computing and Applications
Mining Multidimensional Data through Element Oriented Analysis

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An application of time-changing feature selection

Data Mining
Neural modeling for time series: A statistical stepwise method for weight elimination

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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 nonlinear 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.