average crosscorrelation coefficient

简明释义

平均互相关系数;

英英释义

例句

1.The researchers found a high average crosscorrelation coefficient indicating a strong correlation between temperature and ice cream sales.

研究人员发现高平均互相关系数，表明温度与冰淇淋销售之间存在强相关性。

2.To improve the model's accuracy, we adjusted the parameters based on the average crosscorrelation coefficient obtained from previous experiments.

为了提高模型的准确性，我们根据之前实验获得的平均互相关系数调整了参数。

3.By analyzing the average crosscorrelation coefficient, we can determine how closely related the stock prices are over time.

通过分析平均互相关系数，我们可以确定股票价格随时间的相关程度。

4.The study calculated the average crosscorrelation coefficient to assess the relationship between two time series data sets.

该研究计算了平均互相关系数以评估两个时间序列数据集之间的关系。

5.In signal processing, the average crosscorrelation coefficient helps in identifying patterns between different signals.

在信号处理中，平均互相关系数有助于识别不同信号之间的模式。

作文

In the field of statistics and signal processing, understanding the relationships between different signals or datasets is crucial. One important measure that helps us quantify this relationship is the average crosscorrelation coefficient. The average crosscorrelation coefficient (平均互相关系数) provides a means to evaluate how closely related two signals are over time. It is particularly useful in applications such as time series analysis, where we want to determine if changes in one variable correspond to changes in another variable. To compute the average crosscorrelation coefficient, we first need to gather our data. This could involve collecting time-series data from various sources, such as stock prices, weather patterns, or even audio signals. Once we have our datasets, we can begin the process of calculating the crosscorrelation. This involves taking pairs of observations from both datasets and determining how much one dataset shifts in relation to the other. The result of this calculation gives us a correlation value for each time shift, which we can then average to obtain the average crosscorrelation coefficient. This averaged value indicates the overall strength and direction of the relationship between the two datasets. A value close to 1 implies a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A value around 0 suggests no significant correlation between the datasets. One of the key advantages of using the average crosscorrelation coefficient (平均互相关系数) is that it allows researchers and analysts to identify potential lag effects. For instance, in economic studies, one might find that consumer spending tends to increase after a certain period following an increase in income. By analyzing the average crosscorrelation coefficient, we can pinpoint these lagged relationships and make more informed predictions about future trends. Moreover, the average crosscorrelation coefficient can also help in filtering out noise from data. In many real-world situations, datasets may contain random fluctuations that obscure the underlying relationship we are interested in. By focusing on the average correlation across multiple time shifts, we can reduce the impact of these anomalies and gain a clearer picture of the true relationship between the datasets. In conclusion, the average crosscorrelation coefficient (平均互相关系数) is a powerful tool in the realms of statistics and signal processing. It not only helps us understand the degree of correlation between two datasets but also reveals important insights regarding potential time lags and the influence of external factors. As we continue to collect and analyze data in various fields, mastering the concept of the average crosscorrelation coefficient will undoubtedly enhance our ability to make data-driven decisions and predictions. Understanding this coefficient is essential for anyone involved in data analysis, as it opens up new avenues for exploration and understanding of complex relationships within data.

在统计学和信号处理领域，理解不同信号或数据集之间的关系至关重要。一个帮助我们量化这种关系的重要指标是平均互相关系数。平均互相关系数（average crosscorrelation coefficient）提供了一种评估两个信号随时间变化的相关性的方法。它在时间序列分析等应用中尤为有用，在这些应用中，我们希望确定一个变量的变化是否与另一个变量的变化相对应。要计算平均互相关系数，我们首先需要收集数据。这可能涉及从各种来源收集时间序列数据，例如股票价格、天气模式或甚至音频信号。一旦我们有了数据集，就可以开始计算互相关的过程。这涉及从两个数据集中取出观察值对，确定一个数据集相对于另一个数据集的偏移量。这个计算的结果为每个时间偏移量提供一个相关值，我们可以对其进行平均以获得平均互相关系数。这个平均值指示了两个数据集之间关系的整体强度和方向。接近1的值意味着强正相关，而接近-1的值则表示强负相关。接近0的值则暗示两个数据集之间没有显著的相关性。使用平均互相关系数（average crosscorrelation coefficient）的一个关键优势是，它允许研究人员和分析师识别潜在的滞后效应。例如，在经济研究中，人们可能发现消费者支出在收入增加后的某一段时间内往往会增加。通过分析平均互相关系数，我们可以确定这些滞后关系，并对未来趋势做出更明智的预测。此外，平均互相关系数还可以帮助过滤掉数据中的噪声。在许多现实世界的情况下，数据集可能包含随机波动，这些波动会掩盖我们感兴趣的潜在关系。通过关注多个时间偏移量上的平均相关性，我们可以减少这些异常现象的影响，从而获得数据集之间真实关系的更清晰图景。总之，平均互相关系数（average crosscorrelation coefficient）是统计学和信号处理领域的一种强大工具。它不仅帮助我们理解两个数据集之间的相关程度，还揭示了有关潜在时间滞后和外部因素影响的重要见解。随着我们继续在各个领域收集和分析数据，掌握平均互相关系数的概念无疑将增强我们做出基于数据的决策和预测的能力。理解这个系数对于任何参与数据分析的人来说都是至关重要的，因为它为探索和理解数据中复杂关系的新途径打开了大门。