[scikit-learn] Supervised anomaly detection in time series

[Nicolas Goix]

Hi,

Yes you can use your labeled data (you will need to sub-sample your normal
class to have similar proportion normal-abnormal) to learn your
hyper-parameters through CV.

You can also try to use supervised classification algorithms on `not too
highly unbalanced' sub-samples.

Nicolas

On Thu, Aug 4, 2016 at 5:17 PM, Amita Misra <amisra2 at ucsc.edu> wrote:

> Hi,
>
> I am currently exploring the problem of speed bump detection using
> accelerometer time series data.
> I have extracted some features based on mean, std deviation etc  within a
> time window.
>
> Since the dataset is highly skewed ( I have just 5  positive samples for
> every > 300 samples)
> I was looking into
>
> One ClassSVM
> covariance.EllipticEnvelope
> sklearn.ensemble.IsolationForest
>
> but I am not sure how to use them.
>
> What I get from docs
> separate the positive examples and train using only negative examples
>
> clf.fit(X_train)
>
> and then
> predict the positive examples using
> clf.predict(X_test)
>
>
> I am not sure what is then the role of positive examples in my training
> dataset or how can I use them to improve my classifier so that I can
> predict better on new samples.
>
>
> Can we do something like Cross validation to learn the parameters as in
> normal binary SVM classification
>
> Thanks,?
> Amita
>
> Amita Misra
> Graduate Student Researcher
> Natural Language and Dialogue Systems Lab
> Baskin School of Engineering
> University of California Santa Cruz
>
>
>
>
>
> --
> Amita Misra
> Graduate Student Researcher
> Natural Language and Dialogue Systems Lab
> Baskin School of Engineering
> University of California Santa Cruz
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20160804/b664cd93/attachment.html>