Ricardo Dintén^*, Marta Zorrilla

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 239-265, 2025, DOI:10.32604/cmes.2025.065461 - 31 July 2025

Abstract Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events, posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing, which frequently leads to the development of large and complex models. Inspired by the success of Large Language Models (LLMs), transformer-based foundation models have been developed for time series (TSFM). These models have been proven to reconstruct time series in a zero-shot manner, being able to capture different patterns that effectively characterize time series. This paper proposes the use of TSFM to generate… More >

Shui Liu^1,2, Ke Xiong^1,2,*, Yeshen Li^1,2, Zhifei Zhang^1,2,*, Yu Zhang³, Pingyi Fan⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5077-5093, 2025, DOI:10.32604/cmc.2025.064880 - 30 July 2025

Abstract Accurate prediction of cloud resource utilization is critical. It helps improve service quality while avoiding resource waste and shortages. However, the time series of resource usage in cloud computing systems often exhibit multidimensionality, nonlinearity, and high volatility, making the high-precision prediction of resource utilization a complex and challenging task. At present, cloud computing resource prediction methods include traditional statistical models, hybrid approaches combining machine learning and classical models, and deep learning techniques. Traditional statistical methods struggle with nonlinear predictions, hybrid methods face challenges in feature extraction and long-term dependencies, and deep learning methods incur high… More >

Naif Alsharabi^1,*, Akashdeep Bhardwaj^2,*

Journal of Cyber Security, Vol.7, pp. 135-163, 2025, DOI:10.32604/jcs.2025.064019 - 18 June 2025

Abstract In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive More >

Chunhao Zhang^1,2, Bin Xie^2,3,*, Zhibin Huo¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 843-860, 2025, DOI:10.32604/cmc.2025.063151 - 09 June 2025

Abstract Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poor temporal dependency handling, and suboptimal real-time performance, sometimes even neglecting the temporal relationships between data. To address these issues and improve anomaly detection performance by better capturing temporal dependencies, we propose an unsupervised time series anomaly detection method, VLT-Anomaly. First, we enhance the Variational Autoencoder (VAE) module by redesigning its network structure to better suit anomaly detection through data reconstruction. We introduce hyperparameters to control… More >

Zhengdao Yang¹, Xuewei Wang², Yuling Chen^1,*, Hui Dou¹, Haiwei Sang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5181-5197, 2025, DOI:10.32604/cmc.2025.060564 - 19 May 2025

Abstract Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these More >

Qingqing Song^1,*, Shaoliang Xia¹, Zhen Wu²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2619-2642, 2025, DOI:10.32604/cmc.2025.062526 - 16 April 2025

Abstract Load time series analysis is critical for resource management and optimization decisions, especially automated analysis techniques. Existing research has insufficiently interpreted the overall characteristics of samples, leading to significant differences in load level detection conclusions for samples with different characteristics (trend, seasonality, cyclicality). Achieving automated, feature-adaptive, and quantifiable analysis methods remains a challenge. This paper proposes a Threshold Recognition-based Load Level Detection Algorithm (TRLLD), which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics. By utilizing distribution density uniformity, the algorithm classifies data points and ultimately… More >

Hamza Murad Khan¹, Anwar Khan^1,*, Santos Gracia Villar^2,3,4, Luis Alonso Dzul Lopez^2,5,6, Abdulaziz Almaleh⁷, Abdullah M. Al-Qahtani⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3369-3388, 2025, DOI:10.32604/cmc.2025.060474 - 16 April 2025

Abstract Traffic forecasting with high precision aids Intelligent Transport Systems (ITS) in formulating and optimizing traffic management strategies. The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity. To address this problem, this paper uses the Tree-structured Parzen Estimator (TPE) to tune the hyperparameters of the Long Short-term Memory (LSTM) deep learning framework. The Tree-structured Parzen Estimator (TPE) uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples. This ensures fast convergence in… More >

Nataliya Shakhovska, Volodymyr Shymanskyi^*, Maksym Prymachenko

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4469-4484, 2025, DOI:10.32604/cmc.2025.062675 - 06 March 2025

Abstract Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets More >

Bin Sun¹, Yinuo Wang¹, Tao Shen^1,*, Lu Zhang¹, Renkang Geng²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4219-4236, 2025, DOI:10.32604/cmc.2025.060567 - 06 March 2025

Abstract Traffic datasets exhibit complex spatiotemporal characteristics, including significant fluctuations in traffic volume and intricate periodical patterns, which pose substantial challenges for the accurate forecasting and effective management of traffic conditions. Traditional forecasting models often struggle to adequately capture these complexities, leading to suboptimal predictive performance. While neural networks excel at modeling intricate and nonlinear data structures, they are also highly susceptible to overfitting, resulting in inefficient use of computational resources and decreased model generalization. This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks… More >

Dalal AL-Alimi¹, Mohammed A. A. Al-qaness^2,3,*, Robertas Damaševičius^4,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3539-3561, 2025, DOI:10.32604/cmc.2025.059869 - 17 February 2025

Abstract Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions… More >