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I've been performing some research, in order to find the best approach to identify break points (trend direction change) in a dataset (with pairs of x/y coordinates), that allow me to identify the trend lines behind my data collections.
However I had no luck, finding anything that brings me some light.
The yellow dots in the following image, represent the breakpoints I need to detect.
Any suggestion about an article, algorithm, or implementation example (typescript prefered) would be very helpful and appreciated.
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The breakpoint is the lower value of the range that entitles a customer to a discount. For example, if you offer a one dollar discount per unit for each order between 200 and 299 units, the breakpoint is 200 units.
Break point analysis is a way of looking at customer satisfaction data to determine when there are shifts or breaks in satisfaction levels.
Usually, people tend to filter the data by looking only maximums (support) or only minimums (resistance). A trend line could be the average of those. The breakpoints are when the data crosses the trend, but this gives a lot of false breakpoints. Because images are better than words, you can look at page 2 of http://www.meacse.org/ijcar/archives/128.pdf.
There are a lot of scripts available look for "ZigZag" in
https://www.tradingview.com/
e.g. https://www.tradingview.com/script/lj8djt1n-ZigZag/ https://www.tradingview.com/script/prH14cfo-Trend-Direction-Helper-ZigZag-and-S-R-and-HH-LL-labels/
Also you can find an interesting blog post here (but code in in python):
https://towardsdatascience.com/programmatic-identification-of-support-resistance-trend-lines-with-python-d797a4a90530
with code available: https://pypi.org/project/trendln/
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