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  • Open Access

    REVIEW

    Unsupervised Time Series Segmentation: A Survey on Recent Advances

    Chengyu Wang, Xionglve Li, Tongqing Zhou, Zhiping Cai*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2657-2673, 2024, DOI:10.32604/cmc.2024.054061 - 15 August 2024

    Abstract Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD,… More >

  • Open Access

    ARTICLE

    Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

    Mengmeng Zhao1,2,3, Haipeng Peng1,2,*, Lixiang Li1,2, Yeqing Ren1,2

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2815-2837, 2024, DOI:10.32604/cmc.2024.053765 - 15 August 2024

    Abstract In the Industrial Internet of Things (IIoT), sensors generate time series data to reflect the working state. When the systems are attacked, timely identification of outliers in time series is critical to ensure security. Although many anomaly detection methods have been proposed, the temporal correlation of the time series over the same sensor and the state (spatial) correlation between different sensors are rarely considered simultaneously in these methods. Owing to the superior capability of Transformer in learning time series features. This paper proposes a time series anomaly detection method based on a spatial-temporal network and… More >

  • Open Access

    REVIEW

    An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges

    Nidhi Parashar1, Prashant Johri1, Arfat Ahmad Khan5, Nitin Gaur1, Seifedine Kadry2,3,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

    Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

  • Open Access

    ARTICLE

    A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

    Weijian Song1,, Xi Li1,, Peng Chen1,*, Juan Chen1, Jianhua Ren2, Yunni Xia3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563 - 08 July 2024

    Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

  • Open Access

    ARTICLE

    Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

    Maie Aboghazalah1,*, Passent El-kafrawy2, Abdelmoty M. Ahmed3, Rasha Elnemr5, Belgacem Bouallegue3, Ayman El-sayed4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, DOI:10.32604/cmc.2023.039936 - 20 June 2024

    Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several More >

  • Open Access

    ARTICLE

    TSCND: Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

    Haoran Huang, Weiting Chen*, Zheming Fan

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024

    Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

  • Open Access

    ARTICLE

    Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

    Ying Su1, Morgan C. Wang1, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189 - 26 March 2024

    Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing… More >

  • Open Access

    ARTICLE

    Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

    Rui Wang1, Yao Zhou3,*, Guangchun Luo1, Peng Chen2, Dezhong Peng3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3011-3027, 2024, DOI:10.32604/cmes.2023.047065 - 11 March 2024

    Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations More >

  • Open Access

    ARTICLE

    Defect Detection Model Using Time Series Data Augmentation and Transformation

    Gyu-Il Kim1, Hyun Yoo2, Han-Jin Cho3, Kyungyong Chung4,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324 - 27 February 2024

    Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding… More >

  • Open Access

    ARTICLE

    A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM

    Zhenxiang He*, Xunxi Wang, Chunwei Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607 - 30 January 2024

    Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

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