exponentially-weighted moving average

简明释义

指数加权移动平均方法

英英释义

例句

1.The exponentially-weighted moving average 指数加权移动平均 can be adjusted by changing the smoothing factor to prioritize different time periods.

通过改变平滑因子，可以调整指数加权移动平均 exponentially-weighted moving average，以优先考虑不同的时间段。

2.The exponentially-weighted moving average 指数加权移动平均 helped the company identify trends in customer purchase behavior over time.

该指数加权移动平均 exponentially-weighted moving average 帮助公司识别客户购买行为随时间变化的趋势。

3.In time series forecasting, the exponentially-weighted moving average 指数加权移动平均 is preferred for its responsiveness to recent changes.

在时间序列预测中，指数加权移动平均 exponentially-weighted moving average 因其对近期变化的敏感性而受到青睐。

4.To reduce noise in the data, the researcher applied an exponentially-weighted moving average 指数加权移动平均 to the experimental results.

为了减少数据中的噪声，研究人员对实验结果应用了指数加权移动平均 exponentially-weighted moving average。

5.The stock analyst used the exponentially-weighted moving average 指数加权移动平均 to smooth out short-term fluctuations in the stock price.

股票分析师使用了指数加权移动平均 exponentially-weighted moving average 来平滑股票价格中的短期波动。

作文

In the realm of data analysis and time series forecasting, various techniques are employed to make sense of historical data and predict future trends. One such method is the exponentially-weighted moving average, which has gained popularity due to its effectiveness in smoothing out fluctuations in data while giving more weight to recent observations. This technique is particularly useful in fields such as finance, economics, and environmental science, where understanding trends over time is crucial.The exponentially-weighted moving average (EWMA) is a statistical tool that calculates the average of a dataset while applying an exponential decay factor to the weights assigned to past observations. Unlike a simple moving average, which treats all data points equally, the EWMA allows for more recent data to have a greater influence on the average. This is achieved by applying a smoothing factor, often denoted by the Greek letter alpha (α), which ranges between 0 and 1. A higher value of alpha gives more weight to recent data, making the average more responsive to changes.To illustrate how the exponentially-weighted moving average works, consider a scenario in stock market analysis. Investors often look at historical stock prices to determine trends and make predictions about future movements. By using the EWMA, analysts can quickly identify shifts in stock performance without being overly influenced by older data that may no longer be relevant. For instance, if a stock has been experiencing a steady increase in price recently, the EWMA will reflect this trend more accurately than a simple moving average would, which might still be affected by older, lower prices.One of the key advantages of using the exponentially-weighted moving average is its ability to react to sudden changes in data. In many real-world scenarios, data can be volatile, with abrupt spikes or drops. The EWMA's focus on recent data allows it to adapt quickly to these changes, providing a more timely reflection of current conditions. This characteristic makes it particularly valuable in industries like finance, where rapid decision-making is often required.However, it is important to note that the choice of the smoothing factor alpha can significantly impact the results of the exponentially-weighted moving average. A small alpha value will produce a smoother average that is less sensitive to recent fluctuations, while a larger alpha will create a more volatile average that reacts swiftly to changes. Therefore, selecting the appropriate alpha based on the specific context and requirements of the analysis is crucial.In conclusion, the exponentially-weighted moving average is a powerful tool for analyzing time series data, allowing analysts and decision-makers to gain insights into trends while accounting for the importance of recent observations. Its ability to smooth out noise and respond to changes makes it an essential technique in various fields, particularly where timely information is critical. As data continues to grow in complexity and volume, mastering techniques like the EWMA will be increasingly important for those seeking to leverage data-driven insights effectively.

在数据分析和时间序列预测领域，采用各种技术来理解历史数据并预测未来趋势。其中一种方法是指数加权移动平均，由于其在平滑数据波动方面的有效性而受到广泛欢迎，同时对近期观察值给予更多权重。这种技术在金融、经济和环境科学等领域尤为重要，因为了解随时间变化的趋势至关重要。指数加权移动平均（EWMA）是一种统计工具，它在计算数据集的平均值时，对过去观察值施加指数衰减因子。与简单移动平均不同，后者对所有数据点一视同仁，EWMA允许较新的数据对平均值产生更大的影响。这是通过应用平滑因子（通常用希腊字母α表示，范围在0到1之间）来实现的。较高的α值赋予最近数据更多权重，使得平均值对变化更具响应性。为了说明指数加权移动平均的工作原理，考虑一个股市分析的场景。投资者通常会查看历史股价以确定趋势并预测未来走势。通过使用EWMA，分析师可以快速识别股票表现的变化，而不被可能不再相关的旧数据所过度影响。例如，如果一只股票最近价格稳步上涨，EWMA将比简单移动平均更准确地反映这一趋势，后者可能仍受旧的较低价格影响。使用指数加权移动平均的一个主要优势是它能够对数据的突然变化作出反应。在许多现实场景中，数据可能是波动的，伴随着突发的尖峰或下降。EWMA对近期数据的关注使其能够快速适应这些变化，从而提供对当前状况的及时反映。这一特性使其在金融等行业中尤为有价值，因为在这些行业中，迅速的决策往往是必需的。然而，需要注意的是，平滑因子α的选择会显著影响指数加权移动平均的结果。较小的α值会生成一个更加平滑的平均值，对近期波动的敏感度较低，而较大的α值则会产生一个更加波动的平均值，迅速对变化做出反应。因此，根据分析的具体背景和需求选择合适的α值至关重要。总之，指数加权移动平均是分析时间序列数据的强大工具，使分析师和决策者能够获得对趋势的洞察，同时考虑到近期观察的重要性。其平滑噪声和响应变化的能力使其成为各个领域的重要技术，尤其是在及时信息至关重要的情况下。随着数据的复杂性和数量的不断增长，掌握像EWMA这样的技术对于那些希望有效利用数据驱动见解的人来说将变得越来越重要。