Results 51 to 60 of about 3,979,328 (298)
Out-of-Distribution Representation Learning for Time Series Classification [PDF]
International Conference on Learning Representations, 2022 Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions.
Wang Lu +4 more
semanticscholar +1 more source
Discriminative Dictionary Learning for Time Series Classification
IEEE Access, 2020 Time series symbolization based on the Symbolic Fourier Approximation (SFA) and a sliding window mechanism can effectively improve classification performance. Hence, it has become a research hotspot of time series representation learning.
Wei Zhang +3 more
doaj +1 more source
Invariant Time-Series Classification [PDF]
, 2012 Time-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical diagnosis up to financial econometrics. Support Vector Machines (SVMs) are reported to perform non-optimally in the domain of time series, because they suffer ...
Grabocka, Josif +2 more
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Time series classification based on triadic time series motifs [PDF]
International Journal of Modern Physics B, 2019 It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series.
Wen-Jie Xie, Rui-Qi Han, Wei-Xing Zhou
openaire +3 more sources
Emotional Expression Classification Using Time-Series Kernels [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013 Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures.
Lorincz, A +4 more
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Time series classification with ensembles of elastic distance measures [PDF]
, 2015 Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems.
A Stefan +14 more
core +1 more source
Rethinking Attention Mechanism in Time Series Classification [PDF]
Information Sciences, 2022 Attention-based models have been widely used in many areas, such as computer vision and natural language processing. However, relevant applications in time series classification (TSC) have not been explored deeply yet, causing a significant number of TSC
Bowen Zhao +4 more
semanticscholar +1 more source
Hierarchical Factor Classification of Dendrochronological Time-Series
Annals of Silvicultural Research, 2020 In this paper, Hierarchical Factor Classification (HFC), an exploratory method of classification of characters is introduced, in comparison with Principal Component Analysis (PCA) in order to show its advantages, in particular when dealing with time ...
Sergio Camiz +3 more
doaj +1 more source
Time Series Classification Based on Multi-Dimensional Feature Fusion
IEEE Access, 2023 Time series classification is a key problem in data mining, most of existing classification methods directly extract one-dimensional data from one-dimensional features, which cannot effectively express the inter-relation between different time points ...
Shuo Quan +4 more
doaj +1 more source
Time series classification with Echo Memory Networks [PDF]
Neural Networks, 2019 Echo state networks (ESNs) are randomly connected recurrent neural networks (RNNs) that can be used as a temporal kernel for modeling time series data, and have been successfully applied on time series prediction tasks. Recently, ESNs have been applied to time series classification (TSC) tasks.
Qianli Ma +3 more
openaire +5 more sources