Results 151 to 160 of about 20,272,779 (202)

Classification trees for time series

Pattern Recognition, 2012
This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities.
Douzal-Chouakria, Ahlame, Amblard, Cécile   +1 more
openaire   +3 more sources

Scalable and Accurate Time Series Classification

, 2022
This thesis focuses on time series classification, which aims to develop algorithms that learn to categorize temporally ordered data. It is an important area of machine learning research with a diverse range of applications, such as the classification of satellite images, medical and human activity data.
AHMED SHIFAZ (11925272)
openaire   +2 more sources

Time Series Classification at Scale

, 2019
This thesis develops scalable algorithms and techniques to classify large amount of time series data. Nowadays, many real-world applications are generating huge amount of time series data. This wealth of data is required to create finer and more accurate classification models that allow us to learn from the data.
CHANG WEI TAN (6527420)
openaire   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Early classification on time series

Knowledge and Information Systems, 2011
In this paper, we formulate the problem of early classification of time series data, which is important in some time-sensitive applications such as health informatics. We introduce a novel concept of MPL (minimum prediction length) and develop ECTS (early classification on time series), an effective 1-nearest neighbor classification method.
Zhengzheng Xing, Jian Pei 0001, Philip S. Yu   +2 more
openaire   +1 more source

On the Blind Classification of Time Series

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
We propose a cord distance in the space of dynamical models that takes into account their dynamics, including transients, output maps and input distributions. In data analysis applications, as opposed to control, the input is often not known and is inferred as part of the (blind) identification.
Alessandro Bissacco, Stefano Soatto
openaire   +1 more source

Time series envelopes for classification

2010 5th IEEE International Conference Intelligent Systems, 2010
In this paper we considered a streaming data classification problem. First we introduced a concept of upper and lower envelopes of time series in order to reduce dimensionality of them. Next we merged machine learning tools like feedforward neural networks for selection principal attributes as well as decision rules of the form if … then … for time ...
Maciej Krawczak, Grazyna Szkatula
openaire   +1 more source

Time Series Classification of Electroencephalography Data [PDF]

, 2023
Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain using electrodes placed on the scalp. EEG data is commonly used for classification problems. However, many of the current classification techniques are dataset specific and cannot be applied to EEG data problems as a whole. We propose the use of
Aiden Rushbrooke, Jordan Tsigarides, Saber Sami, Anthony J. Bagnall   +3 more
openaire   +2 more sources

Unsupervised time series classification

Signal Processing, 1995
Abstract In this paper a scheme for unsupervised probabilistic time series classification is detailed. The technique utilizes autocorrelation terms as discriminatory features and employs the Volterra Connectionist Model (VCM) to transform the multi-dimensional feature information of each training vector to a one-dimensional classification space. This
Jebu J. Rajan, Peter J. W. Rayner
openaire   +2 more sources

Scalable time series classification

Data Mining and Knowledge Discovery, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Minimizing response time in time series classification

Knowledge and Information Systems, 2015
Providing a timely output is one of the important criteria in applications of time series classification. Recent studies have been motivated to explore models of early prediction, prediction based on truncated temporal observations. The truncation of input improves the response time, but generally reduces the reliability of the prediction.
Shin Ando, Einoshin Suzuki
openaire   +2 more sources