Results 11 to 20 of about 576,901 (299)
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 +2 more
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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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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.
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Time series classification with random temporal features
Journal of King Saud University: Computer and Information Sciences, 2023 Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately ...
Cun Ji +6 more
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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 +4 more
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Efficient Shapelet Discovery for Time Series Classification
, 2022 Time-series shapelets are discriminative subsequences, recently found effective for time series classification (tsc). It is evident that the quality of shapelets is crucial to the accuracy of tsc.
Chun, Kwok Pan +5 more
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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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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
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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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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
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