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  • Open Access

    ARTICLE

    Stock Price Forecasting: An Echo State Network Approach

    Guang Sun1, Jingjing Lin1,*, Chen Yang1, Xiangyang Yin1, Ziyu Li1, Peng Guo1,2, Junqi Sun3, Xiaoping Fan1, Bin Pan1

    Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189

    Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >

  • Open Access

    ARTICLE

    An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

    Ayesha Jabeen1, Sitara Afzal1, Muazzam Maqsood1, Irfan Mehmood2, Sadaf Yasmin1, Muhammad Tabish Niaz3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598

    Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More >

  • Open Access

    ARTICLE

    An Advanced Approach for Improving the Prediction Accuracy of Natural Gas Price

    Quanjia Zuo1, Fanyi Meng1,*, Yang Bai2

    Energy Engineering, Vol.118, No.2, pp. 303-322, 2021, DOI:10.32604/EE.2021.013239

    Abstract As one of the most important commodity futures, the price forecasting of natural gas futures is of great significance for hedging and risk aversion. This paper mainly focuses on natural gas futures pricing which considers seasonality fluctuations. In order to study this issue, we propose a modified approach called six-factor model, in which the influence of seasonal fluctuations are eliminated in every random factor. Using Monte Carlo method, we first assess and comparative analyze the fitting ability of three-factor model and six-factor model for the out of sample data. It is found that six-factor model has better performance than three-factor… More >

  • Open Access

    ARTICLE

    Ontology-Supported Double-Level Model Construction for International Disaster Medical Relief Resource Forecasting

    Min Zhu1,2,3,#, Huiyu Jin1,#, Ruxue Chen1, Quanyi Huang2,3, Shaobo Zhong4, Guang Tian1,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1097-1109, 2020, DOI:10.32604/iasc.2020.010140

    Abstract In a disaster, mass casualties lead to a surge in demand for medical services. Some relief actions have been criticized for being ill-adapted to dominating medical needs. This research established a disaster medical relief planning model in 3 steps. 1. Establishing the two-level conceptual model. 2. Using the ontology method to describe the hierarchy and relating rules of the terms and concepts associated with the model. 3. Using an ontology-support casebased reasoning approach to build the case similarity matching process, which can provide a more efficient system for decision support. A case study validated the model and demonstrated its usage. More >

  • Open Access

    ARTICLE

    Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

    Raisa Nazir Ahmed Kazi1,*, Manjur Kolhar2, Faiza Rizwan2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1237-1250, 2021, DOI:10.32604/cmc.2020.012707

    Abstract The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results indicated that the analysis of… More >

  • Open Access

    ARTICLE

    Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

    Weiwen Kong1, Baowei Wang1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 849-863, 2020, DOI:10.32604/cmes.2020.012818

    Abstract Internet of Things (IoT) is a network that connects things in a special union. It embeds a physical entity through an intelligent perception system to obtain information about the component at any time. It connects various objects. IoT has the ability of information transmission, information perception,andinformationprocessing.Theairqualityforecastinghasalways been an urgent problem, which affects people’s quality of life seriously. So far, many air quality prediction algorithms have been proposed, which can be mainly classifed into two categories. One is regression-based prediction, the other is deep learning-based prediction. Regression-based prediction is aimed to make use of the classical regression algorithm and the various… More >

  • Open Access

    ARTICLE

    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More >

  • Open Access

    ARTICLE

    Deep Learning Approach with Optimizatized Hidden-Layers Topology for Short-Term Wind Power Forecasting

    Xing Deng1,2, Haijian Shao1,2,*

    Energy Engineering, Vol.117, No.5, pp. 279-287, 2020, DOI:10.32604/EE.2020.011619

    Abstract Recurrent neural networks (RNNs) as one of the representative deep learning methods, has restricted its generalization ability because of its indigestion hidden-layer information presentation. In order to properly handle of hidden-layer information, directly reduce the risk of over-fitting caused by too many neuron nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis (Isodata), to divide the similarity of raw data points, and maps… More >

  • Open Access

    ARTICLE

    Wind Speed Prediction Modeling Based on the Wavelet Neural Network

    Zhenhua Guo1,2, Lixin Zhang1,*, Xue Hu1, Huanmei Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 625-630, 2020, DOI:10.32604/iasc.2020.013941

    Abstract Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short-term wind speed is the basis for predicting wind power. This paper proposes a short-term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short-term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the mean square error and regression.… More >

  • Open Access

    ARTICLE

    Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

    Omer Berat Sezer*, Ahmet Murat Ozbayoglu

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065

    Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our… More >

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