Andreu, Javier and Angelov, Plamen (2010) Forecasting Time-Series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with On-line Inputs Selection. In: 2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010). IEEE, New York, pp. 1479-1483. ISBN 978-1-4244-6920-8Full text not available from this repository.
In this paper we present results and algorithm used to predict 14 days horizon from a number of time series provided by the NN GC1 concerning transportation datasets . Our approach is based on applying the well known Evolving Takagi-Sugeno (eTS) Fuzzy Systems [2-6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their structure from the data stream on-line and in real-time mode. That means we used all the data samples from the time series only once, at any instant in time we only used one single input vector (which consist of few data samples as described below) and we do not iterate or memorize the whole sequence. It should be emphasized that this is a huge practical advantage which, unfortunately cannot be compared directly to the other competitors in NN GC1 if only precision/error is taken as a criteria. It is also worth to require time for calculations and memory usage as well as iterations and computational complexity to be provided and compared to build a fuller picture of the advantages the proposed technique offers. Nevertheless, we offer a computationally light and easy to use approach which in addition does not require any user-or problem-specific thresholds or parameters to be specified. Additionally, this approach is flexible in terms not only of its structure (fuzzy rule based and automatic self-development), but also in terms of automatic input selection as will be described below.
|Item Type:||Contribution in Book/Report/Proceedings|
|Additional Information:||"©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."|
|Uncontrolled Keywords:||predictive models ; eTS|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited By:||Dr. Plamen Angelov|
|Deposited On:||28 Jul 2010 17:01|
|Last Modified:||10 Dec 2016 02:46|
Actions (login required)