| Submission declined on 19 June 2023 by Wpscatter (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
Comment: The sources given do not establish notability over general smoothing algorithms, which is already an article on Wikipedia. Please review the notability guidelines and ensure you have at least 3 reliable sources which establish notability of this subject. Consider adding info to the Smoothing article instead. Also, Wikipedia articles do not typically include "benefits and drawbacks" or "conclusion" sections, and certainly not when they're unsourced. Review our guidelines on writing in encyclopedic tone. WPscatter t/c 17:30, 19 June 2023 (UTC)
Weighted smoothing methods are set of statistical strategies for smoothing or removing fluctuations or noise in data while keeping significant properties of data in statistical models. This category is a special case of general smoothing with unequal weights. The approach gives each data point a weight, which defines how much that data point contributes to the smoothed data. Weighted smoothing can generate a smoother curve that more accurately portrays the underlying trend in the data by giving greater weights to data points that are judged more relevant and lower weights to data points that are deemed less essential. The exponential smoothing is a forecasting technique and does not fit into current category. Categorization such that methods utilizes equal weights, in other words, general smoothing methods are excluded as from our discussion.
Applications edit
Weighted smoothing is frequently utilized in a variety of sectors, including finance, economics, engineering, and science. Weighted smoothing, for example, is frequently used in finance to assess stock prices, commodity prices, and other financial patterns. Weighted smoothing is used in economics to examine economic time series data such as GDP, inflation rates, and employment rates. Weighted smoothing is a signal processing technique used in engineering to reduce noise from data. Applying locally weighted smoothing techniques on scatter plots which is called Lowess or Loess smoothing, is another important use case in many applications. [1]
Methods edit
Weighted smoothing may be implemented using a variety of approaches, including weighted moving average, weighted least squares which is also called weighted linear regression, local regression and smoothing splines. Each approach has benefits and weaknesses and is employed in different applications depending on the data and analysis needs.
Linear Regression with Weights
Weighted linear regression is a sort of regression analysis in which each data item is assigned a weight based on its value. Using these weights, the regression equation is then solved to obtain a smoothed curve that captures the underlying trend in the data.
The weighted linear regression of a set of data points, (x_1, y_1), (x_2, y_2), ..., (x_n, y_n), with weights, w_1, w_2, ..., w_n, is given by:
where y is the smoothed data, x is the independent variable, b_0 is the intercept, and b_1 is the slope of the regression line. The intercept and slope are solved using the weighted least squares method with the weights, w_1, w_2, ..., w_n.
Triangular smoothing
This method is similar to rectangular smoothing except that utilizes unequal weights.[2]
Benefits and Drawbacks edit
Weighted smoothing provides various advantages, including the ability to maintain significant elements of the data, eliminate noise from the data, and generate a smoother curve that more closely portrays the underlying trend in the data. However, there are significant disadvantages to weighted smoothing, including the necessity to establish the weights for each data point, which can be subjective, and the possibility to inject bias into the smoothed data if the weights are not accurately given.
Conclusion edit
Weighted smoothing is a strong statistical technique for smoothing or reducing noise from data while keeping significant data properties. The approach is extensively used in many domains and may be implemented using a variety of techniques such as weighted moving average, weighted linear regression, and weighted least squares. Despite its shortcomings, weighted smoothing is a crucial technique for evaluating time series data and constructing smooth curves that capture the underlying trend in the data.
See also edit
References edit
- ^ Isnanto, R. Rizal. "Comparision on Several Smoothing Methods in Nonparametric Regression" (PDF). Retrieved 19 June 2023.
- ^ O'Haver, T. (Jun 2023). "Smoothing". terpconnect.umd.edu.