CODEWITHSUNDEEP
scikit-learntime-seriessvm
Exp5LogMingus
Exp5LogMingus

Reputation: 33

Using SVM to perform classification on multi-dimensional time series datasets

I would like to use scikit-learn's svm.SVC() estimator to perform classification tasks on multi-dimensional time series - that is, on time series where the points in the series take values in R^d, where d > 1.

The issue with doing this is that svm.SVC() will only take ndarray objects of dimension at most 2, whereas the dimension of such a dataset would be 3. Specifically, the shape of a given dataset would be (n_samples, n_features, d).

Is there a workaround available? One simple solution would just be to reshape the dataset so that it is 2-dimensional, however I imagine this would lead to the classifier not learning from the dataset properly.

Upvotes: 0

Views: 1712

Answers (1)

MB-F
MB-F

Reputation: 23637

Without any further knowledge about the data reshaping is the best you can do. Feature engineering is a very manual art that depends heavily on domain knowledge.

As a rule of thumb: if you don't really know anything about the data throw in the raw data and see if it works. If you have an idea what properties of the data may be beneficial for classification, try to work it in a feature.

Say we want to classify swiping patterns on a touch screen. This closely resembles your data: We acquired many time series of such patterns by recording the 2D position every few milliseconds.

In the raw data, each time series is characterized by n_timepoints * 2 features. We can use that directly for classification. If we have additional knowledge we can use that to create additional/alternative features.

Let's assume we want to distinguish between zig-zag and wavy patterns. In that case smoothness (however that is defined) may be a very informative feature that we can add as a further column to the raw data.

On the other hand, if we want to distinguish between slow and fast patterns, the instantaneous velocity may be a good feature. However, the velocity can be computed as a simple difference along the time axis. Even linear classifiers can model this easily so it may turn out that such features, although good in principle, do not improve classification of raw data.

If you have lots and lots and lots and lots of data (say an internet full of good examples) Deep Learning neural networks can automatically learn features to some extent, but let's say this is rather advanced. In the end, most practical applications come down to try and error. See what features you can come up with and try them out in practice. And beware the overfitting gremlin.

Upvotes: 1

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