prathapkumarbaratam / mrsqm

MrSQM: Fast Time Series Classification with Symbolic Representations

MrSQM (Multiple Representations Sequence Miner) is a time series classifier. The MrSQM method can quickly extract features from multiple symbolic representations of time series and train a linear classification model with logistic regression. The method has four variants with four different feature selection strategies:

• MrSQM-R: Random feature selection.
• MrSQM-RS: MrSQM-R with a follow-up Chi2 test to filter less important features.
• MrSQM-S: Pruning the all-subsequence feature space with a Chi2 bound and selecting the optimal set of top k subsequences.
• MrSQM-SR: Random sampling of the features from the output of MrSQM-S.

Installation

Dependencies

cython >= 0.29
numpy >= 1.18
pandas >= 1.0.3
scikit-learn >= 0.22
fftw3 (http://www.fftw.org/)

Download the repository:

git clone https://github.com/mlgig/mrsqm.git

Move into the code directory of the repository:

cd mrsqm/mrsqm

Build package from source using:

pip install .

Example

Load data from arff files

X_train,y_train = util.load_from_arff_to_dataframe("data/Coffee/Coffee_TRAIN.arff")
X_test,y_test = util.load_from_arff_to_dataframe("data/Coffee/Coffee_TEST.arff")

Train with MrSQM

clf = MrSQMClassifier()
clf.fit(X_train,y_train)

Make predictions

predicted = clf.predict(X_test)

More examples can be found in the example directory. The full UEA and UCR Archive can be downloaded from http://www.timeseriesclassification.com/.

This repository provides supporting code, results and instructions for reproducing the work presented in our publication (under review):

"MrSQM: Fast Time Series Classification with Symbolic Representations and Efficient Sequence Mining", Thach Le Nguyen and Georgiana Ifrim