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Search Results (13)
  • Open Access


    Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

    Zihao Song, Yan Zhou*, Wei Cheng, Futai Liang, Chenhao Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034

    Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed. Our model consists of two… More >

  • Open Access


    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

    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 in 1D space. Additionally, a… More >

  • Open Access


    CALTM: A Context-Aware Long-Term Time-Series Forecasting Model

    Canghong Jin1,*, Jiapeng Chen1, Shuyu Wu1, Hao Wu2, Shuoping Wang1, Jing Ying3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230

    Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

  • Open Access


    Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

    Wonyong Chung, Jaeuk Moon, Dongjun Kim, Eenjun Hwang*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5817-5836, 2023, DOI:10.32604/cmc.2023.036830

    Abstract Multivariate time-series forecasting (MTSF) plays an important role in diverse real-world applications. To achieve better accuracy in MTSF, time-series patterns in each variable and interrelationship patterns between variables should be considered together. Recently, graph neural networks (GNNs) has gained much attention as they can learn both patterns using a graph. For accurate forecasting through GNN, a well-defined graph is required. However, existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes, and consider all nodes with the same weight when constructing graph. In this paper, we propose a novel graph construction method that solves aforementioned limitations.… More >

  • Open Access


    Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

    Subhajit Chatterjee1, Yung-Cheol Byun2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5507-5525, 2023, DOI:10.32604/cmc.2023.032843

    Abstract The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive… More >

  • Open Access


    Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

    Saleh Albahli1,*, Farman Hassan2, Ali Javed2,3, Aun Irtaza2,4

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 833-849, 2022, DOI:10.32604/cmc.2022.024267

    Abstract COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world.… More >

  • Open Access


    Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

    Ganapathy Pattukandan Ganapathy1, Kathiravan Srinivasan2, Debajit Datta2, Chuan-Yu Chang3,4,*, Om Purohit5, Vladislav Zaalishvili6, Olga Burdzieva6

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6333-6350, 2022, DOI:10.32604/cmc.2022.023254

    Abstract A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the… More >

  • Open Access


    Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

    N. Ashwini1,*, V. Nagaveni2, Manoj Kumar Singh3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 893-908, 2022, DOI:10.32604/iasc.2022.022231

    Abstract Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns, a neural model, suffered heavily… More >

  • Open Access


    Heart Sound Analysis for Abnormality Detection

    Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

    Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A… More >

  • Open Access


    Uncertainty Analysis on Electric Power Consumption

    Oakyoung Han1, Jaehyoun Kim2,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2621-2632, 2021, DOI:10.32604/cmc.2021.014665

    Abstract The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields. However, unpredictable phenomena, such as extreme weather events or the novel coronavirus 2019 (COVID-19) outbreak, can greatly limit the ability of time-series analyses to establish reliable patterns. The present work addresses this issue by applying uncertainty analysis using a probability distribution function, and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul, South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic… More >

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