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  • Open Access


    A TMA-Seq2seq Network for Multi-Factor Time Series Sea Surface Temperature Prediction

    Qi He1, Wenlong Li1, Zengzhou Hao2, Guohua Liu3, Dongmei Huang1, Wei Song1,*, Huifang Xu4, Fayez Alqahtani5, Jeong-Uk Kim6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 51-67, 2022, DOI:10.32604/cmc.2022.026771

    Abstract Sea surface temperature (SST) is closely related to global climate change, ocean ecosystem, and ocean disaster. Accurate prediction of SST is an urgent and challenging task. With a vast amount of ocean monitoring data are continually collected, data-driven methods for SST time-series prediction show promising results. However, they are limited by neglecting complex interactions between SST and other ocean environmental factors, such as air temperature and wind speed. This paper uses multi-factor time series SST data to propose a sequence-to-sequence network with two-module attention (TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMA-Seq2seq is an LSTM-based encoder-decoder architecture facilitated by… More >

  • Open Access


    A Hybrid Neural Network-based Approach for Forecasting Water Demand

    Al-Batool Al-Ghamdi1,*, Souad Kamel2, Mashael Khayyat3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1365-1383, 2022, DOI:10.32604/cmc.2022.026246

    Abstract Water is a vital resource. It supports a multitude of industries, civilizations, and agriculture. However, climatic conditions impact water availability, particularly in desert areas where the temperature is high, and rain is scarce. Therefore, it is crucial to forecast water demand to provide it to sectors either on regular or emergency days. The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions. This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future. Focusing on the… More >

  • Open Access


    Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

    Nguyen Thanh Hoan1, Nguyen Van Dung1, Ho Le Thu1, Hoa Thuy Quynh1, Nadhir Al-Ansari2,*, Tran Van Phong3, Phan Trong Trinh3, Dam Duc Nguyen4, Hiep Van Le4, Hanh Bich Thi Nguyen4, Mahdis Amiri5, Indra Prakash6, Binh Thai Pham4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1431-1449, 2022, DOI:10.32604/cmes.2022.018699

    Abstract Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best… More >

  • Open Access


    Wavelet Based Detection of Outliers in Volatility Time Series Models

    Khudhayr A. Rashedi1,2,*, Mohd Tahir Ismail1, Abdeslam Serroukh3, S. Al wadi4

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3835-3847, 2022, DOI:10.32604/cmc.2022.026476

    Abstract We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wavelet Transform (MODWT) to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers. Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform (DWT). The series sample size does not need to be a power of 2 and the… More >

  • Open Access


    Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

    Vineetha Mathai*, P. Indumathi

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

    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 fading models considered. To accomplish… More >

  • Open Access


    Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

    J. Faritha Banu1,*, S. B. Rajeshwari2, Jagadish S. Kallimani2, S. Vasanthi3, Ahmed Mateen Buttar4, M. Sangeetha5, Sanjay Bhargava6

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176

    Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as… More >

  • Open Access


    Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

    S. Manikandan1,*, B. Nagaraj2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 761-775, 2022, DOI:10.32604/iasc.2022.023398

    Abstract Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective… More >

  • Open Access


    An Advanced Integrated Approach in Mobile Forensic Investigation

    G. Maria Jones1,*, S. Godfrey Winster2, P. Valarmathie3

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 87-102, 2022, DOI:10.32604/iasc.2022.022972

    Abstract Rapid advancement of digital technology has encouraged its use in all aspects of life, including the workplace, education, and leisure. As technology advances, so does the number of users, which leads to an increase in criminal activity and demand for a cyber-crime investigation. Mobile phones have been the epicenter of illegal activity in recent years. Sensitive information is transferred due to numerous technical applications available at one’s fingertips, which play an essential part in cyber-crime attacks in the mobile environment. Mobile forensic is a technique of recovering or retrieving digital evidence from mobile devices so that it may be submitted… More >

  • Open Access


    Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

    W. A. Shaikh1,2,*, S. F. Shah2, S. M. Pandhiani3, M. A. Solangi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1517-1532, 2022, DOI:10.32604/cmes.2022.017822

    Abstract This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. Wavelet Decomposition (WD) algorithm has been combined with various traditional forecasting time-series models, such as Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS) and their effects are examined in terms of the statistical estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the wavelet combined models are classy… 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 >

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