[scikit-learn] Supervised anomaly detection in time series

[Amita Misra]

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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