differencing

简明释义

英[ˈdɪf(ə)rənsiŋ]美[ˈdɪfərənsiŋ]

n. 差分，[数] 差分化

英英释义

单词用法

同义词

反义词

例句

1.The pixel-value differencing (PVD) steganography has the superiority of high capacity and good imperceptibility, but the original PVD method is vulnerable to histogram analysis.

像素差值）隐写算法具有嵌入容量高和不可见性好的优点。

2.In this paper, a new method of moving-people detection based on fusion of coterminous frames differencing and background subtraction is proposed.

提出一种基于对称帧间差分与背景减除相结合的运动目标检测和自适应背景更新方法。

3.A new algorithm for license plate character segmentation based on differencing projection and preferably segmented characters is proposed.

针对车牌字符分割提出一种基于差分投影与优割字符的车牌字符分割算法。

4.Selects the differencing algorithm to be used for impact flash detection.

选择差分算法，用于检测的影响闪光。

5.Based on the detailed study of image pixel-value differencing, a novel and efficient adaptive information hiding algorithm was proposed.

在深入研究图像像素灰度差分的基础上，提出了一种新颖有效的自适应信息隐藏算法。

6.The half-point differencing method is used to get more accurate results.

为使计算结果更加准确，采用了半点差分格式离散。

7.Use of differencing techniques in the analysis of GPS data.

差分技术在全球定位系统数据分析中的运用。

8.To identify changes between imports and to manage model updates, you can import the XMI file into a new Rhapsody project and then use the differencing and merging capability.

为了识别导入之间的变更，并管理更新，您可以导入XML文件到一个新的Rhapsody项目中，然后使用差异和合并功能。

9.The blueprints along with their patented object differencing technology together are used to dramatically increase response times.

这个蓝图，连同他们的对象查分技术，被用于显著提高响应速度。

10.In time series analysis, differencing is often used to make the data stationary.

在时间序列分析中，differencing 通常用于使数据平稳。

11.After differencing, the autocorrelation function showed significant improvement.

经过differencing后，自相关函数显示出显著改善。

12.By applying differencing, we can better identify seasonal patterns in the data.

通过应用differencing，我们可以更好地识别数据中的季节性模式。

13.The first step in our model was differencing the dataset to remove trends.

我们模型的第一步是对数据集进行differencing以去除趋势。

14.The analyst recommended differencing to avoid spurious regression results.

分析师建议使用differencing以避免虚假回归结果。

作文

In the field of statistics and data analysis, the term differencing refers to a technique used to transform a time series dataset. This process involves computing the differences between consecutive observations in order to remove trends and seasonality from the data. The primary goal of differencing is to stabilize the mean of the time series by eliminating changes that are not constant over time. This is particularly important when working with non-stationary data, where the statistical properties vary over time.For instance, consider a scenario where a researcher is analyzing monthly sales data for a retail store. If the sales figures show an upward trend due to seasonal effects or market growth, simply using the raw data may lead to misleading conclusions. By applying differencing, the analyst can focus on the changes in sales from one month to the next, rather than the absolute sales values. This allows for a clearer understanding of the underlying patterns and fluctuations in the data.The process of differencing can be done in multiple ways. The most common method is first-order differencing, where the difference between each observation and its predecessor is calculated. For example, if the sales in January were $1000 and in February they were $1200, the first-order difference would be $200. This simple calculation helps to highlight the growth or decline in sales without the influence of the overall trend.In some cases, second-order differencing may be necessary, particularly when the data exhibits more complex patterns. This involves taking the difference of the differences, which can help to further eliminate any remaining trends. For example, if the first-order differences show a consistent increase, applying second-order differencing could reveal whether this increase is accelerating or decelerating.One of the significant advantages of differencing is that it prepares the data for further analysis, such as forecasting or modeling. Many statistical models, including ARIMA (AutoRegressive Integrated Moving Average), require stationary data as input. By employing differencing, analysts can ensure that their datasets meet this requirement, thus improving the reliability of their predictions.However, it is important to note that differencing can also have drawbacks. Over-differencing can lead to the loss of valuable information regarding the original dataset's structure. Therefore, it is crucial for analysts to carefully assess the need for differencing and to apply it judiciously. Additionally, visualizing the data before and after differencing can provide insights into the effectiveness of the transformation.In conclusion, differencing is a vital technique in time series analysis that aids in making non-stationary data stationary. By focusing on the changes between observations rather than the absolute values, analysts can derive meaningful insights and improve the accuracy of their models. As data continues to grow in complexity, mastering techniques like differencing will remain essential for researchers and practitioners across various fields, ensuring that they can effectively analyze and interpret their data.

在统计学和数据分析领域，术语differencing指的是一种用于转换时间序列数据集的技术。这个过程涉及计算连续观察值之间的差异，以去除数据中的趋势和季节性。differencing的主要目标是通过消除随时间变化的非恒定变化来稳定时间序列的均值。当处理非平稳数据时，这一点尤为重要，因为统计特性会随时间而变化。例如，考虑一个研究者正在分析零售店的月销售数据。如果销售数字因季节性影响或市场增长而显示出上升趋势，单纯使用原始数据可能会导致误导性的结论。通过应用differencing，分析师可以专注于每个月销售额的变化，而不是绝对销售值。这使得对数据中潜在模式和波动的理解更加清晰。differencing的过程可以通过多种方式进行。最常见的方法是一次差分differencing，即计算每个观察值与其前一个值之间的差异。例如，如果一月的销售额为1000美元，而二月为1200美元，则一次差分为200美元。这一简单的计算有助于突出销售的增长或下降，而不受整体趋势的影响。在某些情况下，可能需要二次差分differencing，特别是当数据表现出更复杂的模式时。这涉及到差分的差分计算，这可以帮助进一步消除任何剩余的趋势。例如，如果一次差分显示出持续增加，应用二次差分differencing可能会揭示这种增加是加速还是减速。differencing的一个显著优势是它为进一步分析（如预测或建模）准备了数据。许多统计模型，包括自回归积分滑动平均模型（ARIMA），要求输入数据为平稳数据。通过采用differencing，分析师可以确保他们的数据集满足这一要求，从而提高预测的可靠性。然而，需要注意的是，过度差分differencing可能会导致丢失原始数据集结构中有价值的信息。因此，分析师必须仔细评估differencing的必要性，并谨慎应用。此外，在差分前后可视化数据可以提供有关变换有效性的见解。总之，differencing是时间序列分析中的一项重要技术，有助于使非平稳数据变为平稳。通过关注观察值之间的变化而不是绝对值，分析师可以获得有意义的见解，并提高模型的准确性。随着数据复杂性的不断增长，掌握像differencing这样的技术将对各个领域的研究人员和从业者至关重要，确保他们能够有效分析和解释数据。