منابع مشابه
Clustering of Multivariate Time-Series Data
A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is based on the Mahalanobis distance between ...
متن کاملClustering and Visualization of Multivariate Time Series
The analysis of MTS is an established research area, and methods to carry it out have stemmed both from traditional statistics and from the Machine Learning and Computational Intelligence fields. In this chapter, we are mostly interested in the latter, but considering a mixed approach that can be ascribed to Statistical Machine Learning. MTS are often analyzed for prediction and forecasting and...
متن کاملAn Empirical Comparison of Distance Measures for Multivariate Time Series Clustering
Multivariate time series (MTS) data are ubiquitous in science and daily life, and how to measure their similarity is a core part of MTS analyzing process. Many of the research efforts in this context have focused on proposing novel similarity measures for the underlying data. However, with the countless techniques to estimate similarity between MTS, this field suffers from a lack of comparative...
متن کاملClustering of Time Series Data
Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, which take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Chemometrics
سال: 2005
ISSN: 0886-9383,1099-128X
DOI: 10.1002/cem.945