smoothed value

简明释义

修匀值

英英释义

例句

1.The analyst calculated the smoothed value for the stock prices to reduce volatility.

分析师计算了股票价格的平滑值以减少波动性。

2.In time series analysis, a smoothed value helps in identifying trends more clearly.

在时间序列分析中，平滑值有助于更清晰地识别趋势。

3.The smoothed value of temperature readings shows a more accurate climate trend.

温度读数的平滑值显示了更准确的气候趋势。

4.Using exponential smoothing, we derived the smoothed value for our forecasting model.

通过指数平滑法，我们得出了预测模型的平滑值。

5.We applied a moving average to get the smoothed value of the sales data.

我们应用移动平均法来获得销售数据的平滑值。

作文

In the world of data analysis and statistics, one often encounters various techniques to interpret data effectively. One such technique is the use of a smoothed value, which plays a crucial role in making sense of noisy data. The concept of a smoothed value refers to a modified version of the original data that reduces fluctuations and highlights trends more clearly. This is particularly important in fields such as finance, meteorology, and economics, where raw data can be highly volatile and misleading. By applying smoothing techniques, analysts can derive insights that may otherwise remain obscured by erratic data points.To illustrate the significance of a smoothed value, consider the example of stock market prices. Stock prices can fluctuate wildly due to various factors such as market sentiment, economic indicators, and political events. If an investor were to look solely at the daily stock prices, they might struggle to identify an underlying trend. However, by calculating the smoothed value using methods like moving averages, the investor can obtain a clearer picture of the stock's performance over time. Moving averages, for instance, take the average of a set number of previous days' prices, providing a smoother line that trends upwards or downwards rather than bouncing erratically.Another common application of smoothed values is in weather forecasting. Meteorologists often deal with vast amounts of data collected from various sources, including satellites, weather stations, and buoys. This data can be noisy, with sudden spikes and drops caused by temporary weather phenomena. By applying smoothing algorithms, meteorologists can produce a smoothed value for temperature or precipitation, allowing them to make more accurate predictions. For instance, instead of reporting the temperature at a specific moment, they might present a smoothed temperature value that reflects the overall trend, giving people a better understanding of what to expect in the coming days.The process of obtaining a smoothed value can involve different techniques, such as exponential smoothing, kernel smoothing, or spline smoothing. Each method has its own advantages and is suited for different types of data. Exponential smoothing, for example, gives more weight to recent observations, making it particularly useful for time series data that may exhibit trends or seasonal patterns. On the other hand, kernel smoothing is often employed in non-parametric statistics, where the goal is to estimate the probability density function of a random variable without making strong assumptions about its distribution.Despite its benefits, the use of smoothed values also comes with challenges. One major concern is the potential loss of important information. While smoothing can help clarify trends, it can also obscure significant fluctuations that may be critical for decision-making. Therefore, analysts must strike a balance between reducing noise and retaining essential data points. This is especially true in fields like healthcare, where changes in patient data can indicate urgent needs for intervention.In conclusion, the concept of a smoothed value is vital in data analysis, enabling clearer interpretation of complex datasets. Whether in finance, meteorology, or any other field that relies on data, the ability to derive a smoothed value allows professionals to identify trends and make informed decisions. However, it is essential to apply smoothing techniques judiciously, ensuring that while we enhance our understanding of the data, we do not overlook critical information that could impact outcomes significantly.

在数据分析和统计学的世界中，人们经常会遇到各种技术来有效地解读数据。其中一种技术就是使用平滑值，它在减少波动并更清晰地突出趋势方面起着至关重要的作用。平滑值的概念是指原始数据的修改版本，它减少了波动并更清晰地突出趋势。这在金融、气象和经济等领域尤为重要，因为原始数据可能高度波动且误导性强。通过应用平滑技术，分析师可以得出可能被杂乱的数据点所掩盖的见解。为了说明平滑值的重要性，考虑股票市场价格的例子。由于市场情绪、经济指标和政治事件等各种因素，股票价格可能会剧烈波动。如果投资者仅查看每日股票价格，他们可能会难以识别潜在趋势。然而，通过计算使用移动平均法获得的平滑值，投资者可以更清楚地了解股票的表现。例如，移动平均法取前几天价格的平均值，提供了一条更平滑的线，而不是上下波动。平滑值的另一个常见应用是在天气预报中。气象学家通常处理来自各种来源（包括卫星、气象站和浮标）收集的大量数据。这些数据可能很嘈杂，突然的峰值和下降可能是由临时天气现象引起的。通过应用平滑算法，气象学家可以生成温度或降水量的平滑值，使他们能够做出更准确的预测。例如，他们可能不会报告特定时刻的温度，而是呈现反映整体趋势的平滑温度值，从而使人们更好地理解未来几天的预期。获得平滑值的过程可以涉及不同的技术，例如指数平滑、核平滑或样条平滑。每种方法都有其优点，并适用于不同类型的数据。例如，指数平滑对最近的观察赋予更多权重，这使其特别适用于可能表现出趋势或季节模式的时间序列数据。另一方面，核平滑通常用于非参数统计，其中目标是估计随机变量的概率密度函数，而不对其分布做出严格假设。尽管有其好处，但使用平滑值也面临挑战。一个主要问题是重要信息可能会丢失。虽然平滑可以帮助澄清趋势，但也可能掩盖可能对决策至关重要的显著波动。因此，分析师必须在减少噪音和保留基本数据点之间找到平衡。这在医疗保健等领域尤其如此，因为患者数据的变化可能表明需要紧急干预。总之，平滑值的概念在数据分析中至关重要，使复杂数据集的解释更加清晰。无论是在金融、气象还是任何其他依赖数据的领域，提取平滑值的能力使专业人士能够识别趋势并做出明智的决策。然而，必须谨慎应用平滑技术，确保在增强我们对数据的理解时，不会忽视可能显著影响结果的关键信息。