Results 171 to 180 of about 20,272,779 (202)

Embedding Time Series Data for Classification

2005
We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a ...
Akira Hayashi, Yuko Mizuhara, Nobuo Suematsu   +2 more
openaire   +1 more source

Sequential classification of MODIS time series

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
In this paper a sequential time-varying maximum likelihood classifier is applied on coarse resolution MODIS surface reflectance data. It is shown that good class separability can be obtained after considering only one year of data. Finally it is shown that after NDVI, band 2 has the highest separability of all the MODIS land bands, and band 6 has the ...
Trienko L. Grobler, Etienne R. Ackermann, Augustinus J. van Zyl, Waldo Kleynhans, Brian P. Salmon, Jan C. Olivier   +5 more
openaire   +1 more source

Fusion Architectures for the Classification of Time Series

2001
The classification of time series based on local features is discussed in this paper. In this context we discuss the topics data fusion, decision fusion, and temporal fusion. Three different classifier architectures for these fusion tasks are proposed.
Christian Dietrich 0002, Friedhelm Schwenker, Günther Palm   +2 more
openaire   +1 more source

Reliable early classification of time series

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Early classification of time series is important in time-sensitive applications. An approach is presented for early classification using generative classifiers with the dual objectives of providing a class label as early as possible while guaranteeing with high probability that the early class matches the class that would be assigned to a longer time ...
Hyrum S. Anderson, Nathan Parrish, Kristi Tsukida, Maya R. Gupta   +3 more
openaire   +2 more sources

Stacking for multivariate time series classification

Pattern Analysis and Applications, 2013
This work presents a novel approach to multivariate time series classification. The method exploits the multivariate structure of the time series and the possibilities of the stacking ensemble method. The basics of the method may be described in three steps: first, decomposing the multivariate time series on its constituent univariate time series ...
Oscar J. Prieto, Carlos J. Alonso-González, Juan José Rodríguez Diez   +2 more
openaire   +1 more source

Model-Based Time Series Classification

2014
We propose MTSC, a filter-and-refine framework for time series Nearest Neighbor (NN) classification. Training time series belonging to certain classes are first modeled through Hidden Markov Models (HMMs). Given an unlabeled query, and at the filter step, we identify the top K models that have most likely produced the query.
Alexios Kotsifakos, Panagiotis Papapetrou   +1 more
openaire   +2 more sources

Classification of short time series

2009
Many time series are of short duration because data acquisition has, of necessity, proceeded for but a brief term. Such data have previously often been analyzed by methods that either do not explicitly take into account time related changes or that are designed for long time series.
openaire   +2 more sources

Time Series Classification with Representation Ensembles

2015
Time series has attracted much attention in recent years, with thousands of methods for diverse tasks such as classification, clustering, prediction, and anomaly detection. Among all these tasks, classification is likely the most prominent task, accounting for most of the applications and attention from the research community.
Rafael Giusti, Diego Furtado Silva, Gustavo E. A. P. A. Batista   +2 more
openaire   +1 more source

Optimal Filtering for Time Series Classification

2015
The application of a (smoothing) filter is common practice in applications where time series are involved. The literature on time series similarity measures, however, seems to completely ignore the possibility of applying a filter first. In this paper, we investigate to what extent the benefit obtained by more complex distance measures may be achieved ...
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Time series clustering and classification

International Journal of Approximate Reasoning
Pierpaolo D'Urso, Livia De Giovanni, Elizabeth Ann Maharaj   +2 more
openaire   +1 more source