As opposed to verifying a time threshold on a method (or series of methods) in a test scenario, JUnitPerf also facilitates load testing.
与在测试场景中验证一个方法(或系列方法)的时间限制正好相反，JUnitPerf也方便了负载测试。
You can even make tests a bit more granular and test a series of methods to ensure they meet a certain time threshold.
甚至可以测试得更加细致一些，可以测试一系列方法来确保它们满足特定的时间限制。
Next, the thesis analysis the characterization of Roundtrip time delay (RTT). The RTT time series collected from the Internet are studied statistically by using both linear and nonlinear methods.
其次，对网络时延(RTT)特性进行了分析，利用线性和非线性方法对从互联网上采集的RTT时间序列进行统计分析。
Objective To explore the methods of disease index time series models with influencing factors.
目的探索带有影响因素的疾病指数时间序列建模方法。
This paper focuses on the application of nonlinear dynamical methods in the analysis of time series.
本文主要研究非线性动力学方法在时间序列分析中的应用。
There are many branches in the field of time series analysis using nonlinear tools, and nonlinear dynamical methods is one of them that springs up in these years.
可以用来研究时间序列的非线性工具有许多种，而其中非线性动力学方法则是近年来兴起的一个重要分支。
Because stock forecasting is a uncertain, nonlinear and nonstationary time series problem, it is difficult to achieve a satisfying prediction effect by traditional methods.
由于股票预测是不确定、非线性、非平稳的时间序列问题，传统的方法往往难以取得满意的预测效果。
Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
The course is the advanced part in a PhD econometrics sequence. It provides developments in theory and methods of nonlinear time series econometrics.
本课程是博士生计量经济学系列课程的高级内容，介绍非线性时间序列的理论和方法的前沿研究。
On the basis of traditional time series analysis and modeling methods, the thesis puts forward a new complete and simply identification method by using ar model.
本文在传统时间序列分析建模方法的基础上，提出了用AR模型的新的完整而又简单的辨识方法。
This paper introduces the methods of calculating the dynamic means according to all sorts of time series, and expounds the interrelation of these methods.
本文介绍了各种时间数列的序时平均数的计算方法，并阐明了各种计算方法之间的内在联系。
The methods for fitting the autoregressive model to the stationary time series are briefly reviewed.
本文首先略述用自回归模式去拟合平稳时间序列的各种方法；
Based on the research and comparison of different methods, this paper explored the similarity search method of time series which is adaptive to the characteristics of hydrological data.
论文在深入研究和比较各种方法的基础上，探索适合水文数据特点的时间序列相似性搜索的方法。
Although the period of using ar, MR, ARMA, ARIMA modeling methods of time series analysis for observed seismic data processing is not long, it is believed that these methods are promising.
时间序列分析中的AR,MR,ARMA,ARIMA等建模方法应用于地震观测资料分析中尚为时不长，是很有发展前途的地震信息处理方法。
In economic field, the time series models are important methods in describing and forecasting the objective economic process.
在经济领域中，运用时间序列模型来进行客观经济过程的描述和预测是一个非常重要的方法。
Many different methods and techniques about time series similarity search have been proposed and successful applications have been made in some fields, such as stock analysis.
目前，国内外学者和研究人员采用不同的方法围绕时间序列相似性的研究已取得了一定的成果，并在股票等领域有了一定的应用。
In this paper, the data of body temperature from experiment is studied by using the methods of hypothesis test and time series analysis, then the mathematical basis of identifying the nature is found.
本文由试验得到的体温数据，分别用假设检验和时间序列分析方法，找到辨别寒热性的数学依据。
Some statistical test methods on "fat tail" distribution of time series are obtained by using properties of extreme value theory and extreme index estimator under large sample.
使用极值理论和极值指数估计量的性质，在大样本的情况下得到序列分布“肥尾”现象的检验方法。
Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.
最后，我们提出了结合时间序列表达数据和静态数据来构建动态调控网络的方法。
Combining the advantages of regression analysis methods and time series forecast model with equal step length, a compound forecasting model was set up , and was tested with engineering data.
结果显示，把最小二乘支持向量机回归预测与等步长时序预测相结合的预测方法应用于地下工程围岩位移监测数据的分析及预测是可行的；
One of the current forecast methods is time series forecast which constructs models according to the historical data before using it to forecast the future.
时间序列预测是预测领域内的一个重要研究方向，时间序列预测是一种根据历史数据构造时间序列模型，再把模型外推来预测未来的一种方法。
Measuring the seasonal variation in a time series, we generally use two methods to calculate seasonal indexes—average of original data, trend adjustment for average.
测定季节变动对时间序列的影响，一般按同期平均法（原资料平均法）和趋势剔除法计算季节比率。
The result of investigation show that the combination of EMD-based and WD-based methods may be more effectively in recognizing the main information of the time series.
研究结果表明，将基于EMD的方法和基于WD的方法有机结合起来应用，可以更有效地识别原时间序列的特征信息。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
电力系统短期负荷预测使用的方法有传统建模方法，诸如时间序列、回归分析等方法。
The time series method is one of common methods for forecasting water consumption. The prediction accuracy on water consumption can be guaranteed by the selection of forecast models.
时间序列法是用水量预测的常用方法，其中预测模型的选择是提高预测精度的关键。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
Therefore in this paper, theory and some methods of multi—variable time series are discussed.
因此本文对多变量时序分析作了一些理论和方法的探索。
The methods, which combine time series analysis and neural networks, are especially studied and applied in the model-unknown nonlinear system.
特别针对模型未知的非线性系统，研究了时间序列分析和神经网络相结合的故障预报方法。
The methods, which combine time series analysis and neural networks, are especially studied and applied in the model-unknown nonlinear system.
特别针对模型未知的非线性系统，研究了时间序列分析和神经网络相结合的故障预报方法。
应用推荐