Multivariate time series classification codes and data sets are online

The codes and data sets for our paper Multivariate Time Series Classification with Learned Discretization are online. Please find the details by clicking the link.

S-MTS (Symbolic Multivariate Time Series) discretizes the observation space in a supervised manner to obtain the symbolic representation for classification. It is mostly implemented in R (uses the randomForest package) and C (time consuming for loops are in C). There is no explicit feature extraction, the features are learned into symbolic representation.

It can handle nominal (categorical) time series and missing values. It is multiclass (does not require training multiple models as in Support Vector Machines (SVM)). It scales well with number of features (variables) and the number of time series. 

A tree-based ensemble (Random forest) is used to learn the symbols. Two parameters are important: Alphabet size and number of trees to generate the symbolic representation. Since each tree is trained on random subsample of the instances and features, different views of the same time series are represented by the ensemble (has some connection to scale-space theory).

The codes of S-MTS are available on

Please let me know if you have any questions by contacting me through the contact link in the menu above.

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