[scikit-learn] Supervised anomaly detection in time series

[Jared Gabor]

Lots of great suggestions on how to model your problem.  But this might be
the kind of problem where you seriously ask how hard it would be to gather
more data.

On Thu, Aug 4, 2016 at 2:17 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
>
>
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