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It is found that the GARCH (1,1) model has a good fitting effect on the time series by the statistical analysis of the logarithmic yield of the closing price of the Shanghai Composite Index. Therefore, this paper first uses the model GARCH (1,1) predicts the daily closing price of the Shanghai Composite Index, and then uses the Fourier series to correct the predicted residuals to obtain the final...
In order to structure the complex nature of the significant wave height time series, Autoregressive Integrated Moving Average model of order (p,1,0) was applied. Each step which is essential for precise modeling is covered comprehensively. First, the stationarity of given time series of SWH with its 1st order difference were checked. Akaike's Information Criteria and Bayesian Information Criteria...
Investing in stocks is one of the most popular approaches for money investment. This paper aims to predict short-term stock prices of SET50 of Stock Exchange of Thailand (SET). The proposed method is called CARIMA (Cross Correlation Autoregressive Integrated Moving Average. The basic idea of CARIMA is to find the most highly correlated s tock t o predict the target one in addition to ARIMA predicted...
Presence of solar energy driven power sources has been increasing with remarkable trends in recent years. Such growth is mostly related to photovoltaic generation and it is a direct consequence of significant fall in costs for this technology. Such generation, together with load and wind power, requires forecasting in day-ahead operation planning. Furthermore, in power system studies, it is often...
As the large-scale wind power is connected to the grid, challenges are brought to the security and stability operation of the power system, and therefore it is significant to predict power of regional multiple wind farms. In addition, there are different degrees of correlation among regional multiple wind farms because of their geographic proximity. In this paper, according to a large number of wind...
This paper attempts to apply the ARMA (Auto Regressive Moving Average) model to predict patients' glucose concentration because of the successful application of time series ARMA model in forecasting the fault rate. This paper gives the glucose concentration prediction method based on ARMA model and the actualization on MATLAB platform. After analyzing the time series of several patients' glucose concentration,...
Wind power prediction has received much attention due to the development renewable energy sources using wind power. The paper presents a new approach which is a support vector regression (SVR) based local predictor (LP) with false neighbours filtered (FNF-SVRLP) to undertake short-term wind power perdition. The proposed predication method not only combines the powerful SVR with the reconstruction...
The popularization of the Internet provide people with more quick and direct access to information channel, the network search data record the netizens' tens of thousands of search concerns and needs to provide the necessary data base for the research of social and economic behavior. Search behaviors' anonymity just can meet the venereal-disease suspected patients' privacy need. This paper will use...
Time series analysis is to explain correlation and the main features of the data in chronological order by using appropriate statistical models. Since the past electricity generated sequence in China shows a strong seasonal variations and several values for January are lost in recent years, estimating the missing values is an important task before building a model. This paper will estimate the missing...
The objective of this paper was to forecast the number of monthly new outbreaks of Swine Pasteurellosis with ARMA model. The forecasting model was constructed using the data from Jan. 2005 to Dec. 2008 and was validated by the data in 2009. The method employed inspection of the run plots and ACF plots in the determination of the order d of differencing and the parameters of ARMA model. Normalized...
Traumatic brain injury (TBI) endangers many patients and lays great burden on the neural intensive-care units in the whole world. To improve the outcome of TBI patients, it is desirable to forecast the intracranial Pressure (ICP) so to enable timely or early interventions to control the ICP level. Past research mainly focused on ICP pulse morphology analysis and ICP waveform forecast, but results...
In recent years, with its sustained and rapid economic development, the contradiction between supply and demand on China's petroleum is daily outstanding, and it makes the dependence rate of foreign oil resources going higher and higher. With the method of time series analysis and based on the data of China's oil self-sufficiency and proven reserves from 1980 to 2009, ARIMA models which is used for...
This paper proposes a radial basis function (RBF) neural network-based model for short-term solar power prediction (SPP). Instead of predicting solar power directly, the model predicts transmissivity, which is then used to obtain solar power according to the extraterrestrial radiation. The proposed model uses a novel two-dimensional (2D) representation for hourly solar radiation and uses historical...
The story of "Walmart Nappies and Beer" inspires researches to find the potential relationship between a focal product (or item/SKU) and some other products (we call supplementary products here) in a supply chain. How to reflect the dependent demand relationship between a supplementary product and a focal product is significant for supply chain management and operations management. This...
In this paper, We first review the generation and development of the volatility used in financial time series. Later, we investigate the application of volatility into real estate investment trusts area. More researchers focus on models of estimation, characteristics and forecast. Analyzed the modeling and empirical results, we find that ultra-high-frequency (UHF) data application in REIT volatility...
In the competitive petroleum markets, oil price forecasting is becoming increasingly relevant to producers and consumers. This paper develops a structural econometric model of the Brent crude spot price using the explanatory variable of defined relative inventory and OPEC production to analyze and forecast short-run oil price. A Hodrick-Prescott filter method presented obtains the relative inventory...
Monthly Malaysia crude oil production data for the period of January 2005 to May 2010 were analyzed using time-series method called Autoregressive Integrated Moving Average (ARIMA) model. Autocorrelation and partial autocorrelation functions were calculated to examine the stationarity of the data. Then, an appropriate Box-Jenkins ARIMA model was fitted. Validity of the model was tested using Box-Pierce...
Magnetic storm is a significant magnetic disturbance, which has some influence in communication system and power system. Its intensity is always measured by DST and it is often predicted by the application of neural network nonlinear simulation and differential equation so on. Most of these methods need the data collected several hours before magnetic storm besides DST data. Here we proposed a new...
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