Relevance Learning for Time Series Inspection
Abstract
By Means of Local Neighborhood Regression and Time Windows, the Generative Topographic Mapping (Gtm) Allows to Predict and Visually Inspect Time Series Data. Gtm itself, However, is Fully Unsupervised. in This Contribution, We Propose an Extension of Relevance Learning to Time Series Regression with Gtm. This Way, the Metric Automatically Adapts According to the Relevant Time Lags Resulting in a Sparser Representation, Improved Accuracy, and Smoother Visualization of the Data.
Recommended Citation
A. Gisbrecht et al., "Relevance Learning for Time Series Inspection," ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 489 - 494, European Symposium on Artificial Neural Networks, Jan 2012.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-287419049-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Jan 2012
