Results 51 to 60 of about 20,272,779 (202)

Non-Parametric Time Series Classification [PDF]

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2006
We present an improved state-based prediction algorithm for time series. Given time series produced by a process composed of different underlying states, the algorithm predicts future time series values based on past time series values for each state. Unlike many algorithms, this algorithm predicts a multi-modal distribution over future values.
Scott Lenser, Manuela M. Veloso
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Multivariate time series classification with temporal abstractions [PDF]

, 2009
The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data.
Hauskrecht, M, Sacchi, Lucia, Bellazzi, R, Bellazzi, Riccardo, Batal, L, Batal , Iyad, Sacchi, L, Hauskrecht, Milos   +7 more
core  

Adaptive feature fusion for time series classification

, 2022
Time series classification is one of the most critical and challenging problems in data mining, which exists widely in various fields and has essential research significance.
Liu, Zhaoying, Zhang, Ting, Li, Yujian, Hussain, Syed Fawad; id_orcid, Waqas, Muhammad, Wang, Tian, Zhaoying Liu, Ting Zhang, Syed Fawad Hussain, Muhammad Waqas, Yujian Li, Tian Wang   +11 more
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Feature-fused residual network for time series classification

Journal of King Saud University: Computer and Information Sciences
In various fields such as healthcare and transportation, accurately classifying time series data can provide important support for decision-making. To further improve the accuracy of time series classification, we propose a Feature-fused Residual Network
Yanxuan Wei, Mingsen Du, Teng Li, Xiangwei Zheng, Cun Ji   +4 more
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LSTM Fully Convolutional Networks for Time Series Classification

IEEE Access, 2018
Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences.
Fazle Karim, Somshubra Majumdar, Houshang Darabi, Shun Chen   +3 more
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Similarity Learning for Time Series Classification

CoRR, 2016
Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably the most common comparison measure. However, not much research effort has been put into improving it by
Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban   +3 more
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Using derivatives in time series classification [PDF]

Data Mining and Knowledge Discovery, 2012
Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected during business, medical or biological operations, for example. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data, which in ...
Tomasz Górecki, Maciej Luczak
openaire   +2 more sources

Direct multi-step estimation and time series classification

, 2014
The AR metric represents a consolidated model-based approach for time series classification. The goodness of the final classification may of course be affected by the misspecification of the models describing the observed time series.
CORDUAS, MARCELLA, Marcella Corduas
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Time Series Classification in Dissimilarity Spaces [PDF]

, 2020
. Time series classification in the dissimilarity space combines the advantages of the dynamic time warping and the rich mathematical structure of Euclidean spaces.
Brijnesh J Jain, Stephan Spiegel
core  

Temporal classification of short time series data

BMC Bioinformatics
Motivation Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time
Benedikt Venn, Thomas Leifeld, Ping Zhang, Timo Mühlhaus   +3 more
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