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Uncertain Time Series Classification [PDF]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods.
openaire   +1 more source

Meta-Feature Fusion for Few-Shot Time Series Classification

IEEE Access, 2023
Deep learning has been widely adopted for end-to-end time-series classification (TSC). However, the effectiveness of deep learning heavily relies on large-scale data.
Seo-Hyeong Park, Nur Suriza Syazwany, Sang-Chul Lee   +2 more
doaj   +1 more source

Time series classification through visual pattern recognition

Journal of King Saud University: Computer and Information Sciences, 2022
In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment).
Agnieszka Jastrzebska
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Time series classification based on arima and adaboost [PDF]

MATEC Web of Conferences, 2020
In this paper, a novel time series classification approach, which using auto regressive integrated moving average model (ARIMA) features and Adaptive Boosting (AdaBoost) classifications.
Wang Jinghui, Tang Shugang
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Time series classification based on statistical features

EURASIP Journal on Wireless Communications and Networking, 2020
This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC.
Yuxia Lei, Zhongqiang Wu
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Multivariate time series classification using kernel matrix

Electronics Letters, 2022
Multivariate time series (MTS) classification is a fundamental problem in time series mining, and the approach based on covariance matrix is an attractive way to solve the classification. In this study, it is noted that a traditional covariance matrix is
Jiancheng Sun, Huimin Niu, Zongqing Tu, Zhinan Wu, Si Chen   +4 more
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Transformer Feature Fusion Network for Time Series Classification [PDF]

Jisuanji kexue, 2023
Model ensemble methods train multiple basic models and use a certain rule to aggregate the output of the basic models for time series classification.However,they mainly focus on two aspects.The first one is which model is chose as the basic mo-del.And ...
DUAN Mengmeng, JIN Cheng
doaj   +1 more source

Transfer learning for time series classification [PDF]

, 2018
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset.
Fawaz, Hassan Ismail, Forestier, Germain, Idoumghar, Lhassane, Muller, Pierre-Alain, Weber, Jonathan   +4 more
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Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [PDF]

Jisuanji kexue, 2022
In recent years,the research on the classification of time series has attracted more and more attention.Advanced time series classification methods are usually based on great feature representations.Shapelet refers to the discriminative subsequences in ...
GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang
doaj   +1 more source

Transformation Based Ensembles for Time Series Classification [PDF]

, 2013
Until recently, the vast majority of data mining time series classification (TSC) research has focused on alternative distance measures for 1-Nearest Neighbour (1-NN) classifiers based on either the raw data, or on compressions or smoothing of the raw ...
Bagnall, A, Davis, L, Hills, J, Lines, J
core   +1 more source