nonstationary

简明释义

英[ˌnɒnˈsteɪʃənəri]美[ˈnɑːnˈsteɪʃənəri]

adj. 非平稳的；非稳定的；非固定的

英英释义

单词用法

同义词

反义词

例句

1.This phase of the filtration process is called nonstationary filtration .

这个过滤阶段称为非稳态过滤。

2.In this paper, a nonstationary model for two dimensional correlative source is given.

本文提出一种二维相关的非平稳信源模型。

3.Based on the features of existing structure, the nonstationary random process model of resistance for existing structure is proposed.

根据服役结构的特点，提出了服役结构抗力的非平稳随机过程模型。

4.Some nonstationary Iterative methods for large linear systems in MATLAB are introduced in this paper. We also introduce the mathematic description of the GMRES method.

本文介绍了MATLAB中求解大型线性方程组常用的非定常迭代法，并以GMRES算法为例介绍了算法的数学描述。

5.Frequency-Hopping signal is a typical nonstationary signal and must adopt nonstationary signal processing methods.

跳频信号是典型的非平稳信号，必须采用非平稳信号处理方法。

6.The paper introduces a prestack depth migration by nonstationary phase shift method suitable for laterally variable velocity.

介绍一种能够适应介质速度横向变化的非稳态相移算子及其叠前深度偏移方法。

7.In time series analysis, we often encounter nonstationary 非平稳的 data that requires differencing to achieve stationarity.

在时间序列分析中，我们经常遇到需要差分以达到平稳性的非平稳的数据。

8.When modeling a nonstationary 非平稳的 signal, it’s crucial to account for its changing mean and variance.

在建模非平稳的信号时，考虑其变化的均值和方差至关重要。

9.The climate data collected over decades shows nonstationary 非平稳的 patterns due to global warming effects.

由于全球变暖的影响，几十年来收集的气候数据显示出非平稳的模式。

10.Researchers must apply specific techniques when dealing with nonstationary 非平稳的 processes in econometrics.

研究人员在处理计量经济学中的非平稳的过程时必须应用特定技术。

11.The financial market is considered nonstationary 非平稳的 due to its changing trends and volatility over time.

由于金融市场的趋势和波动性随时间变化，因此被认为是非平稳的。

作文

In the realm of statistics and time series analysis, the term nonstationary refers to a process whose statistical properties, such as mean and variance, change over time. This concept is crucial for understanding various phenomena in fields like economics, meteorology, and engineering. When we say a time series is nonstationary, we imply that it does not have a constant mean or variance, making it more complex to analyze and predict compared to stationary time series.For instance, consider the stock market. The prices of stocks are often nonstationary because they can be influenced by numerous factors including economic indicators, company performance, and even global events. Over time, the average price of stocks may rise or fall due to these influences, and the volatility can also change, indicating a nonstationary process. Analysts need to account for this nonstationary behavior when making predictions or creating models, as failing to do so can lead to inaccurate forecasts.Moreover, in climate science, temperature records can exhibit nonstationary characteristics. For example, if we look at the average temperatures over decades, we might observe an increasing trend due to climate change. This trend indicates that the mean temperature is not constant, thus categorizing the data as nonstationary. Researchers studying climate patterns must recognize this nonstationary nature to develop effective models for predicting future climate scenarios.To address the challenges posed by nonstationary data, statisticians often employ techniques such as differencing or detrending. Differencing involves subtracting the previous observation from the current observation to stabilize the mean of the time series. Detrending, on the other hand, involves removing trends from the data to focus on the fluctuations around a constant mean. These methods help transform a nonstationary series into a stationary one, allowing for more reliable analysis and forecasting.In conclusion, understanding the concept of nonstationary is essential for anyone working with time series data. It highlights the importance of recognizing changes in statistical properties over time and adapting analytical techniques accordingly. Whether in finance, environmental studies, or any field that relies on time-dependent data, acknowledging and addressing nonstationary behavior can significantly enhance the accuracy and reliability of predictions. As we continue to navigate an increasingly complex world, the ability to manage nonstationary processes will undoubtedly remain a vital skill for researchers and analysts alike.

在统计学和时间序列分析领域，术语nonstationary指的是一个过程，其统计特性（如均值和方差）随时间变化。这个概念对于理解经济学、气象学和工程学等领域的各种现象至关重要。当我们说一个时间序列是nonstationary时，我们意味着它没有恒定的均值或方差，使得它比平稳时间序列更复杂，分析和预测也更具挑战性。例如，考虑股市。股票价格通常是nonstationary的，因为它们可能受到包括经济指标、公司表现甚至全球事件在内的众多因素的影响。随着时间的推移，由于这些影响，股票的平均价格可能会上升或下降，而波动性也可能变化，这表明这是一个nonstationary过程。分析师在进行预测或建立模型时需要考虑这种nonstationary行为，因为如果不这样做，可能会导致不准确的预测。此外，在气候科学中，温度记录可能表现出nonstationary特征。例如，如果我们观察几十年的平均温度，可能会发现由于气候变化，温度呈上升趋势。这一趋势表明，平均温度并不是恒定的，从而将数据归类为nonstationary。研究气候模式的研究人员必须认识到这种nonstationary特性，以便开发有效的模型来预测未来的气候情景。为了应对nonstationary数据带来的挑战，统计学家通常采用差分或去趋势等技术。差分涉及从当前观测值中减去先前观测值，以稳定时间序列的均值。而去趋势则涉及从数据中去除趋势，以专注于围绕恒定均值的波动。这些方法有助于将nonstationary序列转化为平稳序列，从而允许更可靠的分析和预测。总之，理解nonstationary的概念对于任何处理时间序列数据的人来说都是至关重要的。它突显了识别统计特性随时间变化的重要性，并相应地调整分析技术。无论是在金融、环境研究还是任何依赖时间相关数据的领域，承认和处理nonstationary行为都可以显著提高预测的准确性和可靠性。随着我们继续在一个日益复杂的世界中导航，管理nonstationary过程的能力无疑将成为研究人员和分析师必备的重要技能。