基于IEA-ARIMA模型的微博信息传播效率研究

构建一种基于改进欧拉算法的时序模型(IEA-ARIMA),从复杂网络理论和路径两方面选取多个指标,对网络静态拓扑结构进行分析,探索不同网络结构下的微博信息传播效率。得出在球型网络、随机网络和微博网络三种不同结构中,球型网络是最优的传播环境,可显著提升微博信息的传播效率。文中深入探讨网络拓扑结构对信息传播效率的影响,并提供新的预测方法,对提高微博信息的传播效率具有重要意义。

非线性时序建模预测方法初探

非线性时序预测在社会、经济、工程等领域均具有重要的实用意义 ,本文详细介绍了三种常用的方法 ,并在此基础上对他们进行了探讨 :分析各自的特点及适用范围 。

Prediction and Analysis of O_3 based on the ARIMA Model

The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

GAS DETECTING AND FORECASTING VIA TIME SERIES METHOD

The importance and urgency of gas detecting and forecasting in underground coal mining are self-evident. Unfortunately, this problem has not yet been solved thoroughly.In this paper, the author suggests that the time series analysis method be adopted for processing the gas stochastic data. The time series method is superior to the conventional Fourier analysis in some aspects, especially, the time series method possesses forecasting (or prediction) function which is highly valuable for gas monitoring.An example of a set of gas data sampled from a certain foul coal mine is investigated and an AR (3) model is established. The fitting result and the forecasting error are accepted satisfactorily.At the end of this paper several remarks are presented for further discussion.

Effects of Snow Cover on Ground Thermal Regime： A Case Study in Heilongjiang Province of China

The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.

An Empirical Study on the stock Price of Modeling

the model in time series analysis are widely used in the field of economy. We often use the model in time series to analyze data, but without regard to the rationality of the model. In this paper, we introduce and analyze Ping An Of China（601318） shares at the opening price（2013/01/04-2013/07/04）.The model is established by analyzing data. Modeling steps of ARIMA model and GARCH model are presented in this paper. The data whether ARIMA model is suitable by white noise. Or the data whether GARCH model is suitable by since the correlation of variance test. By comparing the analysis, it selects a more reasonable model.

Pricing Credit Spread Option with Longstaff-Schwartz and GARCH Models in Chinese Bond Market

This paper investigates the mean-reversion and volatile of credit spread time series by using regression and time series analysis in Chinese bond market. Then the Longstaff-Schwartz model and GARCH model are applied to price credit spread put option. The authors compare the features of these two models by employing daily bond prices of government bonds and corporate bonds for the period 2010–2012 in Chinese bond market. The proposed results show that the higher the credit ratings of the corporate bonds are, the lower the prices of the credit spread options are.

DEPENDENCE ANALYSIS OF REGRESSION MODELS IN TIME SERIES

In this paper, the relative dependence of a linear regression model is studied. In particular, the dependence of autoregressive models in time series are investigated. It is shown that for the first-order non-stationary autoregressive model and the random walk with trend and drift model, the dependence between two states decreases with lag. Some numerical examples are presented as well.