autocorrelation

简明释义

英[/ˌɔːtəʊkəˈreɪləʃən/]美[/ˌɔːtəʊkəˈreɪləʃən/]

n. [数]自相关（作用），[仪]自动校正

英英释义

单词用法

同义词

反义词

例句

1.Based on autocorrelation detection, the appropriate filter is used in image pretreatment, to improve the detection result.

基于自相关检测，提出采用合适的滤波器对含水印图像进行预处理，以提高检测效果。

2.In this paper, a new linear prediction model in autocorrelation domain for speech signal is presented.

提出了一种在自相关域对语音信号进行线性预测分析的方法。

3.The effect of lattice filtering is comparable to covariance method, but much better than autocorrelation method.

从效果上看，格点法接近协方差法，但较自相关法有明显地改善。

4.Once a signal is oscillatory, its autocorrelation is also oscillatory.

如果信号振荡，该信号的自相关函数也振荡。

5.This is the most common type of autocorrelation.

这是一般情况下的自相关。

6.The scale of spatial autocorrelation increased gradually from clover land, apple land to farmland.

从苜蓿地、苹果地到农地，空间自相关的尺度逐渐增大。

7.Methods based on spatial frequencies evaluate the coefficients of the autocorrelation function of the texture.

基于空间频率的方法估计纹理的自相关函数。

8.The presence of autocorrelation in residuals can indicate that a model is not adequately capturing the data's structure.

残差中存在自相关性可能表明模型未能充分捕捉数据的结构。

9.Researchers found a high degree of autocorrelation in the temperature data collected over the years.

研究人员发现多年收集的温度数据中存在很高的自相关性。

10.The time series data showed significant autocorrelation, indicating that past values influence future values.

时间序列数据表现出显著的自相关性，表明过去的值会影响未来的值。

11.In finance, autocorrelation can help traders identify trends in stock prices.

在金融领域，自相关性可以帮助交易者识别股票价格的趋势。

12.To improve the accuracy of predictions, we need to account for autocorrelation in our statistical models.

为了提高预测的准确性，我们需要在统计模型中考虑自相关性。

作文

Autocorrelation is a statistical concept that plays a crucial role in time series analysis. It refers to the correlation of a signal with a delayed copy of itself as a function of delay. In simpler terms, it examines how the values of a variable at one point in time are related to its values at previous points in time. Understanding autocorrelation (自相关) is essential for various fields, including economics, finance, and environmental science, as it helps in identifying patterns over time and making predictions based on historical data.One of the primary applications of autocorrelation (自相关) is in the financial markets. Investors and analysts often use this concept to understand stock price movements and trends. For instance, if a stock's price shows high autocorrelation (自相关) over a certain period, it indicates that past prices can be a good predictor of future prices. This information can guide investment decisions, helping investors determine when to buy or sell stocks based on historical performance.In addition to finance, autocorrelation (自相关) is also significant in climate studies. Researchers analyze temperature and precipitation data over time to identify trends and cycles. By calculating the autocorrelation (自相关) of these variables, scientists can determine whether certain weather patterns tend to repeat and how they may change over time. This understanding is vital for predicting future climate conditions and preparing for potential impacts on agriculture, water resources, and disaster management.Moreover, autocorrelation (自相关) is commonly used in signal processing. Engineers utilize this concept to analyze signals and filter out noise. For example, in telecommunications, understanding the autocorrelation (自相关) of a transmitted signal can help improve the clarity and quality of communication. By recognizing patterns in the signal, engineers can design systems that enhance data transmission and reduce errors.However, it is essential to approach autocorrelation (自相关) with caution, as it can sometimes lead to misleading conclusions. For instance, if a dataset exhibits strong autocorrelation (自相关), it may suggest a trend that does not necessarily indicate causation. Analysts must consider other external factors and conduct thorough investigations before making decisions based solely on autocorrelation (自相关) results.In conclusion, autocorrelation (自相关) is a powerful tool for analyzing time-dependent data across various fields. Its ability to reveal relationships within datasets enables researchers and professionals to make informed decisions and predictions. Whether in finance, climate science, or engineering, understanding autocorrelation (自相关) is vital for interpreting historical data and anticipating future trends. As we continue to gather more data in our increasingly complex world, mastering the concept of autocorrelation (自相关) will become even more critical in driving innovation and understanding the dynamics of the systems we study.

自相关是一个统计学概念，在时间序列分析中发挥着至关重要的作用。它指的是信号与其延迟副本之间的相关性，作为延迟的函数。简单来说，它考察一个变量在某一时间点的值与其在之前时间点的值之间的关系。理解自相关（autocorrelation）对经济学、金融学和环境科学等多个领域至关重要，因为它有助于识别时间上的模式，并基于历史数据进行预测。自相关（autocorrelation）的一个主要应用是在金融市场。投资者和分析师常常利用这个概念来理解股票价格的波动和趋势。例如，如果某只股票在一定时期内表现出较高的自相关（autocorrelation），这表明过去的价格可能是未来价格的良好预测。这些信息可以指导投资决策，帮助投资者根据历史表现决定何时买入或卖出股票。除了金融领域，自相关（autocorrelation）在气候研究中也具有重要意义。研究人员分析温度和降水数据，以识别趋势和周期。通过计算这些变量的自相关（autocorrelation），科学家可以确定某些天气模式是否倾向于重复，以及它们如何随时间变化。这种理解对于预测未来气候条件以及为农业、水资源和灾害管理的潜在影响做好准备至关重要。此外，自相关（autocorrelation）通常用于信号处理。工程师利用这一概念分析信号并过滤噪声。例如，在电信领域，理解传输信号的自相关（autocorrelation）可以帮助提高通信的清晰度和质量。通过识别信号中的模式，工程师可以设计出增强数据传输和减少错误的系统。然而，在使用自相关（autocorrelation）时必须谨慎，因为它有时会导致误导性的结论。例如，如果一个数据集表现出强烈的自相关（autocorrelation），这可能表明一种趋势，但并不一定意味着因果关系。分析师必须考虑其他外部因素，并在仅仅依据自相关（autocorrelation）结果做出决策之前进行彻底调查。总之，自相关（autocorrelation）是分析各个领域时间相关数据的强大工具。它揭示数据集内关系的能力使研究人员和专业人士能够做出明智的决策和预测。无论是在金融、气候科学还是工程领域，理解自相关（autocorrelation）对于解释历史数据和预测未来趋势至关重要。随着我们在日益复杂的世界中继续收集更多数据，掌握自相关（autocorrelation）的概念将在推动创新和理解我们研究的系统动态方面变得更加重要。