moving average; rolling average

简明释义

移动平均数

英英释义

例句

1.The stock analyst used a moving average 移动平均 to smooth out the price fluctuations over the past month.

股票分析师使用了一个移动平均 移动平均来平滑过去一个月的价格波动。

2.The moving average 移动平均 helped the team identify the best time to launch their new product.

这个移动平均 移动平均帮助团队识别出推出新产品的最佳时机。

3.To analyze traffic patterns, the city uses a rolling average 滚动平均 over the last week.

为了分析交通模式，城市使用了过去一周的滚动平均 滚动平均。

4.By applying a rolling average 滚动平均, we can better understand the trend in sales data over time.

通过应用滚动平均 滚动平均，我们可以更好地理解销售数据随时间的趋势。

5.Investors often look at the moving average 移动平均 to make informed decisions about buying and selling stocks.

投资者通常查看移动平均 移动平均以做出有关买卖股票的明智决策。

作文

In the field of statistics and data analysis, the concept of moving average (移动平均) or rolling average (滚动平均) is crucial for understanding trends over time. These techniques are widely used in various disciplines, including finance, economics, and even environmental science, to smooth out short-term fluctuations and highlight longer-term trends. The basic idea behind a moving average is to take a set of data points and calculate an average that moves along with the dataset. This allows analysts to see the underlying trend without being distracted by daily variations.For example, consider a stock price that fluctuates daily. By applying a moving average, such as a 10-day moving average, one can calculate the average stock price over the last ten days and plot this on a graph. This line will be smoother than the actual stock price line, making it easier to identify whether the stock is generally trending upwards or downwards. The rolling average serves the same purpose, allowing for continuous updates as new data becomes available.The calculation of a moving average is relatively straightforward. To compute a simple moving average, you sum the values of the dataset over a specified number of periods and then divide by that number. For instance, if you have the daily closing prices of a stock for ten days, you would add those ten prices together and divide by ten to get the average. As each new day’s data comes in, you drop the oldest price from your calculations and include the newest, hence the term 'moving.'There are different types of moving averages, including the simple moving average (SMA), the weighted moving average (WMA), and the exponential moving average (EMA). Each type has its own method of calculation and is used for different purposes. For instance, a weighted moving average gives more importance to recent data points, which can be beneficial when trying to predict future trends based on the most current information.In practical applications, businesses often rely on moving averages to make informed decisions. For example, a retailer may analyze sales data using a rolling average to understand seasonal trends. By examining the sales figures over the past few months, they can identify patterns that inform inventory management and marketing strategies. Similarly, in economic forecasting, policymakers use moving averages to assess economic indicators such as unemployment rates or consumer spending habits, providing insights that guide fiscal policy.Moreover, the use of moving averages extends beyond financial markets and business analytics. Environmental scientists might use rolling averages to track temperature changes over time, helping to identify climate trends and anomalies. The ability to visualize data through moving averages helps in communicating complex information in a more digestible format.In conclusion, the concepts of moving average and rolling average are essential tools in data analysis. They allow us to filter out noise from the data, enabling clearer insights into trends and patterns. Whether in finance, business, or environmental studies, these statistical methods provide valuable frameworks for decision-making and forecasting. Mastering the use of moving averages can significantly enhance one’s analytical skills and improve the quality of data interpretation.

在统计学和数据分析领域，移动平均（移动平均）或滚动平均（滚动平均）的概念对于理解时间趋势至关重要。这些技术被广泛应用于金融、经济甚至环境科学等多个学科，以平滑短期波动并突出长期趋势。移动平均的基本思想是对一组数据点进行平均计算，并随着数据集的移动而变化。这使得分析师能够看到潜在的趋势，而不被日常变化所干扰。例如，考虑一个每日波动的股票价格。通过应用一个如10天的移动平均，可以计算过去十天的平均股票价格并在图表上绘制这条线。这条线将比实际的股票价格线更平滑，从而更容易识别该股票是否总体上呈上升或下降趋势。滚动平均具有相同的目的，允许在新数据可用时进行持续更新。移动平均的计算相对简单。要计算简单的移动平均，您需要在指定的时间段内对数据集的值求和，然后除以该数字。例如，如果您有十天的股票收盘价，您将把这十个价格加在一起并除以十以获得平均值。随着每天新数据的到来，您从计算中删除最旧的价格并包含最新的，因此有了“移动”这个术语。有不同类型的移动平均，包括简单移动平均（SMA）、加权移动平均（WMA）和指数移动平均（EMA）。每种类型都有其自己的计算方法，并用于不同的目的。例如，加权移动平均对最近的数据点赋予更多的重要性，这在尝试根据最新信息预测未来趋势时可能是有益的。在实际应用中，企业通常依靠移动平均做出明智的决策。例如，零售商可能会使用滚动平均分析销售数据，以了解季节性趋势。通过检查过去几个月的销售数据，他们可以识别出有助于库存管理和营销策略的模式。同样，在经济预测中，政策制定者使用移动平均评估经济指标，如失业率或消费者支出习惯，提供指导财政政策的见解。此外，移动平均的使用不仅限于金融市场和商业分析。环境科学家可能会使用滚动平均跟踪温度随时间变化，从而帮助识别气候趋势和异常现象。通过移动平均可视化数据的能力，有助于以更易于理解的格式传达复杂信息。总之，移动平均和滚动平均的概念是数据分析中的基本工具。它们使我们能够过滤掉数据中的噪声，从而更清晰地洞察趋势和模式。无论是在金融、商业还是环境研究中，这些统计方法都提供了有价值的决策和预测框架。掌握移动平均的使用可以显著提高一个人的分析能力，并改善数据解读的质量。