Normalization versus Percent Change

In summary, normalizing data involves re-scaling by the minimum and range of variables to bring all values to a common scale. This process can be used to compare datasets, such as recordings of the same audio source, by ensuring that their shapes are visually similar. However, due to various factors, the peaks and valleys may not exactly line up, but they should closely align.
  • #1
Tone L
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TL;DR Summary
Why doesn't normalized data represent percent change? How can I quantify normalized data?
I have been working with some time series data of spectral signals, each wavelength has a different signal, so I normalize the data so I can plot it effectively. However, I am struggling to quantify the new normalized data. I will give an example below.

Normalizing data often refers to re-scaling by the minimum and range of the variables, to make all the elements lie between 0 and 1 thus bringing all the values of numeric columns in the data set to a common scale.

The equation is defined as, $$x={x_i−x_{min}}{x_{max}−x_{min}}$$, now this will represent a new list of values from 0 to 1.

Now to illustrate my confusion I will present an example, of just one wavelength. At t0 the corresponding normalized value is 0.987 and at ##t_{10}## the value is 0.927.
At t0 the spectral value is 50482 and ##t_{10}## it is 50415. Using the spectral values, the percent difference between t0 and ##t_{10}## is: 0.13% and for the normalized data it is 6.07%.
When I plot the results they visually are identical... Normalizing doesn't re-scale the values, it completely changes them, but visually they look the same. Thus, my confusion. Why is the percent change not equal to one another, thanks a lot guys!
902_slick_bclsky_all_1ps.png

902_slick_bclsky_all_2ps.png
 
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  • #2
When you normalize a dataset and plot it, the shape should look the same. Normalization either reduces a number or magnifies a number.

As an example, you may have two recordings of the same audio source. The first is from a mic close to the source and the other is from a mic far from the source. The first mic dataset would have relatively high db values while the faraway mic would have lower db values. Basically for sound when you double the distance the db value drops by 6db. Looking at the two plots it might not be obvious that they are recordings of the same audio source.

By normalizing both datasets you are then able to compare both datasets and discover that they are recordings from the same audio source as the peaks and valleys should closely line up. However, they may not exactly line up due to various acoustical properties of the space where the recordings occurred but you could reasonably determine that they are recordings of the same audio source.
 

What is normalization and how is it different from percent change?

Normalization is a statistical process that involves transforming data to have a mean of 0 and a standard deviation of 1. This allows for easier comparison between different sets of data. Percent change, on the other hand, is a measure of the relative change in a value over time, expressed as a percentage. It is used to track the growth or decline of a variable over a specific time period.

When should normalization be used instead of percent change?

Normalization should be used when comparing data that have different units or scales. For example, if you want to compare the performance of two stocks, one with a share price of $100 and the other with a share price of $10, normalization would be more appropriate as it would account for the difference in scale. Percent change, on the other hand, would not be as useful in this scenario.

What are the benefits of using normalization?

Normalization allows for easier comparison of data that have different units or scales. It also helps to reduce the impact of outliers, as they can significantly affect the calculation of percent change. Additionally, normalization can improve the accuracy of statistical models and analyses by reducing the effects of multicollinearity.

Are there any limitations to using percent change?

Yes, there are some limitations to using percent change. As mentioned before, outliers can significantly affect the calculation of percent change, making it less reliable. Additionally, percent change does not take into account the starting point of the data, which can lead to misleading conclusions. For example, a 10% increase from 1 to 2 is not the same as a 10% increase from 100 to 110.

Can normalization and percent change be used together?

Yes, normalization and percent change can be used together. In some cases, it may be beneficial to first normalize the data and then calculate percent change to compare the relative changes between different sets of data. However, it is important to carefully consider the data and the purpose of the analysis before deciding on the appropriate method to use.

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