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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...
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...
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...
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...
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...
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...
Traffic prediction is of significant importance for telecommunication network planning and network optimization. In this paper, the traffic series from a certain mobile network in Heilongjiang province in China is studied. The characteristics in respect of both trend and periodicity are explored with autocorrelation function. Based on the characteristics exhibited in the traffic series, multiplicative...
Recently, the Float Car technology is playing a more and more important role in real-time traffic service systems because it can collect real-time traffic information with low cost, high coverage and high efficiency. Meanwhile, the ability to accurately predict travel times in transportation networks is becoming a critical component for many Intelligent Transportation Systems. This paper focuses on...
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models. The modeling and forecasting ability of ARFIMA-FIGARCH model is investigated...
In this paper, the stochastic characteristics of the electric consumption in France are analyzed. It is shown that the load time series exhibit lasting abrupt changes in the stochastic pattern, termed breaks, which need to be accounted for during the modeling process. Thus, a new robust diagnostic approach for which the identification of the breaks is carried out via a robust autocorrelation function...
With the continuous deterioration of the network environment, a variety of viruses, Trojans continue to affect the security of the network. Through the network traffic anomaly detection and analysis can efficiently find problems existing in the network. This paper discusses the network traffic flow data predict and network anomaly detection, network traffic prediction using ARMA model, network anomaly...
Recently, many commercial products, such as Google Trends and Yahoo! Buzz, are released to monitor the past search engine query frequency trend. However, little research has been devoted for predicting the upcoming query trend, which is of great importance in providing guidelines for future business planning. In this paper, a unified solution is presented for such a purpose. Besides the classical...
In this paper, we propose to combine linear prediction coefficients from the autoregressive model (AR) and the time series itself as features for the clustering algorithm. The purpose of the use of the AR model is to realize the importance of dynamic modeling of microarray time series data. We define the distance among the time series profiles using the autoregressive model and use the hierarchical...
This paper presents the use of a basic ARIMA model for network traffic prediction and anomaly detection. Accurate network traffic modeling and prediction are important for network provisioning and problem diagnosis, but network traffic is highly dynamic. To achieve better modeling and prediction it is needed to isolate anomalies from normal traffic variation. Thus, we decompose traffic signals into...
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