New answers tagged

0

Traditional clustering algorithm which uses Euclidean based distance fails to yield good results in high dimensional data due to Curse of dimensionality Because mean distance between data points diverges and looses its meaning which in turn leads to the divergence of the Euclidean distance, the most common distance used for clustering. So if you are using ...


0

Can I make a slightly more general observation? Look at the datasets they test on, and read [a]. There is no read evidence that this idea works, so why bother to implement it? [a] Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress https://arxiv.org/abs/2009.13807


0

GPS data includes positional and time data. If the n+1 position at t+1 is too far away from the n position at t (i.e. d>0.5m for instance), you should be able to detect an anomaly. Same topic about the angle: if the angle between d1 and d2 is grater than a normal value (ex: 2 degree) then it should be considered as an anomaly. You should consider the ...


2

I don't know if my method gives better accuracy than yours but I think you can find some insights from my approach that you can use to further improve your results. Unlike your approach of using an ensemble of models on the entire dataset, I've tried using the fact that we will have clusters of land(continents for example) for a given dataset and hence I've ...


0

If you have a labeled dataset $f(X) =Y$ then you have a supervised learning problem, so you may try to solve it as a "usual" binary classification problem by using metrics like $F1$ or $AUC$ and Cross-validation to evaluate your model's performance, and what I mean by usual is that you do not need to apply something special for anomaly detection ...


Top 50 recent answers are included