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  • Open Access

    ARTICLE

    Functional Causality between Oil Prices and GDP Based on Big Data

    Ibrahim Mufrah Almanjahie1, 2, Zouaoui Chikr Elmezouar1, 2, 3, *, Ali Laksaci1, 2

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 593-604, 2020, DOI:10.32604/cmc.2020.08901 - 01 May 2020

    Abstract This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four More >

  • Open Access

    ARTICLE

    Short-term Forecasting of Air Passengers Based on the Hybrid Rough Set and the Double Exponential Smoothing Model

    Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

    Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that More >

  • Open Access

    ARTICLE

    Forecasting Damage Mechanics By Deep Learning

    Duyen Le Hien Nguyen1, Dieu Thi Thanh Do2, Jaehong Lee2, Timon Rabczuk3, Hung Nguyen-Xuan1,4,*

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 951-977, 2019, DOI:10.32604/cmc.2019.08001

    Abstract We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness More >

  • Open Access

    ARTICLE

    A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

    Qiang Liu1,*, Yanyun Zou2,3, Xiaodong Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

    Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN)… More >

  • Open Access

    ARTICLE

    Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay Filtering Algorithm

    Lihui Wang1, Feng Qi1, Xin Shen2,3,*, Jinliang Huang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 331-348, 2019, DOI:10.32604/cmes.2019.04268

    Abstract Multiple cropping index (MCI) is a very important indicator in crop production and agricultural intensification, which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land. The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS (Moderate-Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index, and the method of no additional authentication More >

  • Open Access

    ARTICLE

    Time Series Analysis for Vibration-Based Structural Health Monitoring: A Review

    Kong Fah Tee 1,*

    Structural Durability & Health Monitoring, Vol.12, No.3, pp. 129-147, 2018, DOI:10.3970/sdhm.2018.04316

    Abstract Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace, mechanical and civil structures. The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection. Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods. In the literature on the structural damage detection, many time series-based methods have been proposed. When a considered time series model approximates the vibration response of… More >

  • Open Access

    ARTICLE

    Method of Time Series Similarity Measurement Based on Dynamic Time Warping

    Lianggui Liu1,*, Wei Li1, Huiling Jia1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

    Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the More >

  • Open Access

    ARTICLE

    An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

    Jieren Cheng1,2, Ruomeng Xu1,*, Xiangyan Tang1, Victor S. Sheng3, Canting Cai1

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

    Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and… More >

  • Open Access

    ARTICLE

    Gauss Process Based Approach for Application on Landslide Displacement Analysis and Prediction

    Zaobao Liu1,2, Weiya Xu1, Jianfu Shao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.84, No.2, pp. 99-122, 2012, DOI:10.3970/cmes.2012.084.099

    Abstract In this paper, the Gauss process is proposed for application on landslide displacement analysis and prediction with dynamic crossing validation. The prediction problem using noisy observations is first introduced. Then the Gauss process method is proposed for modeling non-stationary series of landslide displacements based on its ability to model noisy data. The monitoring displacement series of the New Wolong Temple Landslide is comparatively studied with other methods as an instance to implement the strategy of the Gauss process for predicting landslide displacement. The dynamic crossing validation method is adopted to manage the displacement series so… More >

  • Open Access

    ARTICLE

    Velocity Fluctuations in a Particle-Laden Turbulent Flow over a Backward-Facing Step

    B. Wang1, H.Q. Zhang1, C.K. Chan2, X.L. Wang1

    CMC-Computers, Materials & Continua, Vol.1, No.3, pp. 275-288, 2004, DOI:10.3970/cmc.2004.001.275

    Abstract Dilute gas-particle turbulent flow over a backward-facing step is numerically simulated. Large Eddy Simulation (LES) is used for the continuous phase and a Lagrangian trajectory method is adopted for the particle phase. Four typical locations in the flow field are chosen to investigate the two-phase velocity fluctuations. Time-series velocities of the gas phase with particles of different sizes are obtained. Velocity of the small particles is found to be similar to that of the gas phase, while high frequency noise exists in the velocity of the large particles. While the mean and rms velocities of More >

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