MrSQM: Fast Time Series Classification with Symbolic Representations
Project description
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/)
Installation using pip
pip install mrsqm
Installation from source
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(nsax=0, nsfa=5)
clf.fit(X_train,y_train)
Make predictions
predicted = clf.predict(X_test)
More examples can be found in the example directory, including a Jupyter Notebook with detailed steps for training, prediction and explanation. 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:
"Fast Time Series Classification with Random Symbolic Subsequences", Thach Le Nguyen and Georgiana Ifrim https://project.inria.fr/aaltd22/files/2022/08/AALTD22_paper_5778.pdf
"MrSQM: Fast Time Series Classification with Symbolic Representations and Efficient Sequence Mining", Thach Le Nguyen and Georgiana Ifrim https://arxiv.org/abs/2109.01036
Citation
If you use this work, please cite as:
@article{mrsqm2022,
title={Fast Time Series Classification with Random Symbolic Subsequences},
author={Le Nguyen, Thach and Ifrim, Georgiana},
year={2022},
booktitle = {AALTD},
url = {https://project.inria.fr/aaltd22/files/2022/08/AALTD22_paper_5778.pdf},
publisher={Springer}
}
@article{mrsqm2022-extended,
title={MrSQM: Fast Time Series Classification with Symbolic Representations and Efficient Sequence Mining},
author={Le Nguyen, Thach and Ifrim, Georgiana},
year={2022},
booktitle = {arxvi},
url = {https://arxiv.org/abs/2109.01036},
publisher={}
}
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