Gaoyong Lu¹, Yang Ou¹, Zhihong Wang², Yingnan Qu², Yingsheng Xia², Dibin Tang², Igor Kotenko³, Wei Li^2,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2403-2441, 2025, DOI:10.32604/cmc.2025.068024 - 23 September 2025

Abstract Deep learning (DL) has revolutionized time series forecasting (TSF), surpassing traditional statistical methods (e.g., ARIMA) and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data. This comprehensive survey reviews state-of-the-art DL architectures for TSF, focusing on four core paradigms: (1) Convolutional Neural Networks (CNNs), adept at extracting localized temporal features; (2) Recurrent Neural Networks (RNNs) and their advanced variants (LSTM, GRU), designed for sequential dependency modeling; (3) Graph Neural Networks (GNNs), specialized for forecasting structured relational data with spatial-temporal dependencies; and (4) Transformer-based models, leveraging self-attention mechanisms to… More >

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 >

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 >

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 >

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 >

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 >

Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

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 >

Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 515-532, 2024, DOI:10.32604/cmc.2023.044504 - 30 January 2024

Abstract This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been… More >

Ahmed ElShafee¹, Walid El-Shafai^2,3, Abeer D. Algarni^4,*, Naglaa F. Soliman⁴, Moustafa H. Aly⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408 - 26 May 2023

Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are… More >