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 >

Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684 - 29 April 2023

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, More >

Weifeng Liu^1,2, Xin Yu^1,*, Qinyang Zhao³, Guang Cheng², Xiaobing Hou¹, Shengqi He⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3199-3219, 2023, DOI:10.32604/cmc.2023.032595 - 31 October 2022

Abstract Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission… More >

Narendran Sobanapuram Muruganandam, Umamakeswari Arumugam^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 979-989, 2023, DOI:10.32604/csse.2023.024943 - 15 June 2022

Abstract In forecasting real time environmental factors, large data is needed to analyse the pattern behind the data values. Air pollution is a major threat towards developing countries and it is proliferating every year. Many methods in time series prediction and deep learning models to estimate the severity of air pollution. Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality. This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter (PM) PM2.5. To perform experimental analysis the… More >

M. Mohan^1,*, P. C. Kishore Raja², P. Velmurugan³, A. Kulothungan⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1151-1163, 2023, DOI:10.32604/iasc.2023.026255 - 06 June 2022

Abstract Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular… More >

Vineetha Mathai^*, P. Indumathi

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234 - 24 March 2022

Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other… More >

Mohammad Hadwan^1,2,3,*, Basheer M. Al-Maqaleh⁴ , Fuad N. Al-Badani⁵ , Rehan Ullah Khan^1,3, Mohammed A. Al-Hagery⁶

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4829-4845, 2022, DOI:10.32604/cmc.2022.017824 - 11 October 2021

Abstract

Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient forecasting accuracy. The present study proposes a hybrid forecasting methods to address this need. The proposed method includes three models. The first model is based on the autoregressive integrated moving average (ARIMA) statistical model; the second model is a back propagation neural network (BPNN) with adaptive slope and momentum parameters; and the third model is a hybridization between ARIMA and BPNN (ARIMA/BPNN) and artificial

… More >