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Time Series Classification with InceptionFCN [PDF]
Sensors, 2021 Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last ...
Saidrasul Usmankhujaev +3 more
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Time Series Classification with Shapelet and Canonical Features
Applied Sciences, 2022 Shapelet-based time series classification methods are widely adopted models for time series classification tasks. However, the high computational cost greatly limits the practicability of the Shapelet-based methods. What is more, traditional Shapelet can
Hai-Yang Liu +3 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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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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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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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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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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Technology investigation on time series classification and prediction [PDF]
PeerJ Computer Science, 2022 Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data.
Yuerong Tong +9 more
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