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 >

Jianxin Feng^*, Jianhao Zhang, Ge Cao, Zhiguo Liu, Yuanming Ding

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1049-1068, 2025, DOI:10.32604/cmc.2024.058374 - 03 January 2025

Abstract Multivariate time series forecasting is widely used in traffic planning, weather forecasting, and energy consumption. Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series. However, the decomposition kernel of previous decomposition-based models is fixed, and these models have not considered the differences in frequency fluctuations between components. These problems make it difficult to analyze the intricate temporal variations of real-world time series. In this paper, we propose a series decomposition-based Mamba model, DecMamba, to obtain the intricate temporal dependencies and… More >

Zhe Li, Yun Liang, Jinyu Wang, Yang Gao^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1171-1192, 2025, DOI:10.32604/cmc.2024.057225 - 03 January 2025

Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >

Santiago Bañales^1,2,*, Raquel Dormido¹, Natividad Duro¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 869-907, 2025, DOI:10.32604/cmes.2024.054946 - 17 December 2024

Abstract Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series… More > Graphic Abstract

Byeongcheon Lee¹, Sangmin Kim¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^1,4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1275-1300, 2024, DOI:10.32604/cmc.2024.054826 - 15 October 2024

Abstract In the context of rapid digitization in industrial environments, how effective are advanced unsupervised learning models, particularly hybrid autoencoder models, at detecting anomalies in industrial control system (ICS) datasets? This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things (IoT) devices, which can significantly improve the reliability and safety of these systems. In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to More >

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 >

Mengmeng Zhao^1,2,3, Haipeng Peng^1,2,*, Lixiang Li^1,2, Yeqing Ren^1,2

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 >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

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 >