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Using A Kalman Filter To Make Sense Of Noisy Data

89 pointsby jack7890about 13 years ago

6 comments

sytelusabout 13 years ago
If you want to understand Kalman filter in simplest term, check out the article at <a href="http://credentiality2.blogspot.com/2010/08/simple-kalman-filter-example.html" rel="nofollow">http://credentiality2.blogspot.com/2010/08/simple-kalman-fil...</a>
dredmorbiusabout 13 years ago
Yet another blog which, if visited without JS enabled, displays a rather disconcertingly unrelated page (ticket search).<p>Noscript -&#62; seatgeek.com -&#62; Temp<p>(For anyone else confused by the apparent topic/content discrepancy).<p>And for those interested in a more readable technical explanation, there's the Wikipedia article (linked from the seatgeek blog): <a href="https://duckduckgo.com/lite" rel="nofollow">https://duckduckgo.com/lite</a>
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jack7890about 13 years ago
If there are other folks interested in using this approach, drop us a line. We're happy to try to help other startups make sense of their data.
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essayistabout 13 years ago
Useful.<p>I'd love to find FAAS - Kalman (and other) filtering as a service.<p>For instance: I run daily backups on various databases. I expect the backup size to increase roughly linearly, but I'm just going to look in on the backups at random, likely ignoring them for months at a time.<p>It'd be great to be able to run the backup size series through a filter that would alert me when something unexpected happened, e.g. slope changes significantly or some unusual step change.
jnazarioabout 13 years ago
great post, and thanks for sharing it. i came across the kalman filter after developing an algorithm to detect worm propagation. it applies equally well in that scenario, basically predictions.
derekjaabout 13 years ago
Very nice post, thanks!