Papers by Keyword: ARIMA Model

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Authors: Bin Wang, Wen Ning Hao, Gang Chen, Deng Chao He, Bo Feng
Abstract: Stock index series is Non-stationary, Nonlinear and factors with impact on stock index fluctuation are complex, a time series forecasting model combined ARIMA model and wavelet neural network is presented. The combined model uses BP neural network as the main framework, uses wavelet basis function instead of transfer function in the network, also add some inner factors of the time series mining by ARIMA model, as the part impute of Wavelet Neural Network. So it is more scientific and rational that using inner factors and external other factors. The last simulate experiment shows that the wavelet neural network forecasting model based on ARIMA has higher accuracy than ARIMA model or BP network.
Authors: Ning Wang, Fei Sun, Xiao Hong Shan
Abstract: As an emerging industry of strategic importance, the development of information service industry has been compelling. By analyzing the development process of the information services industry in the past decade, we can learn that the information service is in warm now. Through establishing an ARMIA model, this study draw conclusion that the industry development index will rise steadily and slowly in 2014. The findings can help government, investors, consumer get a close understanding of the Information Service Industry and take it as a basis for decision making.
Authors: Lei Sun, Xian Wu Hao
Abstract: The bridge health monitoring system can collect large amounts of data, but it lacks the trend analysis of monitoring data. This article introduced the method of Time series analysis into the analysis of bridge monitoring data, and adopted ARIMA model in time series analysis of monitoring data, used the least square method for parameter estimation, established the prediction model for bridge deflection, and conducted the goodness of fit test. Take the actual bridge monitoring data as an example, it was demonstrated that the method is feasible in the prediction of bridge condition trend.
Authors: Xing Jin
Abstract: A common method of debris detection to detect the condition of the engine is to establish the relationship between wearing condition and the elements in debris. To accomplish this aim, the ARIMA and Auto-Regressive models should be set up by analyzing the original time series which is established in accordance with the data of debris. After that, the trend of engine wear in a cycle of overhaul can be verified and the best model can be found depending on the situation of real data of debris. So the original time series can be obtained and the time series model to monitor engine condition can be set up. This paper finally provides a reference for monitoring engine conditions and other familiar fields.
Authors: Ying Xiang, Xiao Hong Zhuang
Abstract: International crude oil price is the referential scale of spot crude oil price and refined oil price. This paper made an analysis and prediction of Brent crude oil price by ARIMA model based on its price data from November 2012 to April 2013. It indicated that model ARIMA (1,1,1) possessed good prediction effect and can be used as short-term prediction of International crude oil price.
Authors: Hua Wei Chen, Ji Wen Huang, Bing Li, Shi Dong Fu, Xin Zhang
Abstract: Data mining model is the most important technical basis of the control target decomposition for the most stringent water resources management of Shandong province. K-means clustering model is adopted to analysis the water withdrawal of industrial added value per ten thousand yuan in 2010. Based on the yearly industrial water consumption trend from 1995 to 2010 of 17 municipal-level cities in Shandong province, the ARIMA (p, d, q) model is established through a lot of fitting and optimization and then the regional industrial water demand and water utilization efficiency in 2015 were forecasted. According to the proposed principal and technical route of target decomposition, the industrial water utilization efficiency target in 2015 of the whole province and 17 municipal-level cities are defined respectively.
Authors: Cheng Gao, Min Jiang, Jiao Ying Huang, Xiang Fen Wang
Abstract: In accelerated degradation test, it is essential to establish a suitable degradation path model of the component. In order to predict the life accurately, it is critical to determine the sensitive parameters. A degradation path modeling method based on time series analysis was proposed in this paper. The sensitive parameters were determined by time series stationary test, and a time series ARIMA model was established for the degradation of sensitive parameters. Taking TL431 as an example, the accelerated degradation tests were carried out to investigate the degradation of its performance parameters, and a degradation path model was established for the parameters. The results indicate that the proposed method is feasible for accelerated degradation test.
Authors: Ye Fei Liu, Huan Qi, Su Qin Sun
Abstract: China's needs of energy increased dramatically in these years. In China, Electrical energy are mainly generated by thermal power plants that use coal as fuel, thus electricity supply are linked to the power fuel (coals) storage of power plants. Henan has been changed form an energy exporter province to an energy importer province. Therefore, the fuel storage and supply of power plants are keys to the security of the province's social development, economics and energy supply. Research the margin of power fuel storage and supply can help the policy makers to learn the security conditions and trends of electricity production microscopically, reducing the risks in the power production process, and improving the efficiency of production and the efficiency of energy. Environmental and economic issues brought by the excessive storage can be reduced. This article describes the ideas and development of early warning system for power fuel storage and supply margin of Henan province.
Authors: Hoon Ja Lee, Tae Jin Ahn
Abstract: The efficient management of the agricultural reservoir may well supply stable water for irrigation. In this article, time series analysis has been used for analyzing the storage of water data in Kihung agricultural reservoir that is located in Yongin City, Korea. For analyzing the storage of water data, three models, the ARIMA model, the autoregressive error model, and the dynamic regression model have been used. The result shows that the autoregressive error model is best suited for describing the storage of water data.
Authors: Meng Tao Huang, Xing Mei Gao
Abstract: Temperature is an important parameter representing the motor operating conditions. Temperature monitoring data are time series data, which have strong correlations in time. From the perspective of mathematical statistics, these correlations of time series were analyzed using EViews6.0 software in this paper. Through the establishment of ARIMA model, the motor shell temperature time series was predicted.The final prediction model is ARIMA(1,1,2). Simulation shows that ARIMA model has high predict precision and can be used to predict the temperature of motor shell, which can help to detect equipment failures, avoiding the losses caused by motor faults.
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