Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70127
Title: Similarity retrieval from time-series tropical cyclone observations using a neural weighting generator for forecasting modeling
Authors: Feng, BO
Liu, JNK
Issue Date: 2005
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2005, v. 3683, p. 128-134 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Building a forecasting model for time-series data is a tough but very valuable research topic in recent years. High variation of time-series features must be considered appropriately for an accurate prediction. For weather forecasting, which is continuous, dynamic and chaotic, it’s difficult to extract the most important information present in the knowledge base and determine the importance of each feature. In this paper, taking tropical cyclone (TC) as an example, we present an integrated similarity retrieval model to forecast the intensity of a tropical cyclone using neural network, which is adopted to generate a set of appropriate weights for various associated features of a tropical cyclone. A time adjustment function is used for time-series consideration. The experimental results show that this integrated approach can achieve a better performance.
Description: 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, September 14-16, 2005
URI: http://hdl.handle.net/10397/70127
ISBN: 978-3-540-28896-1
978-3-540-31990-0
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11553939_19
Appears in Collections:Conference Paper

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