Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408282 | Neurocomputing | 2011 | 13 Pages |
Abstract
A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is proposed in this paper. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series. Particle swarm optimization and evolutionary algorithms are then used to remove the noise and identify the skeleton algebraic sequence. Numerical experiments with an artificially generated and a real-world time series are used to illustrate the functionality of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Minvydas Ragulskis, Kristina Lukoseviciute, Zenonas Navickas, Rita Palivonaite,